InternSVG: Towards Unified SVG Tasks with Multimodal Large Language Models
Authors: Haomin Wang
Jinhui Yin
Qi Wei
Wenguang Zeng
Lixin Gu
Shenglong Ye
Zhangwei Gao
Yaohui Wang
Yanting Zhang
Yuanqi Li
Yanwen Guo
Wenhai Wang
Kai Chen
Yu Qiao
Hongjie Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Scalable Vector Graphic
Multimodal Large Language Models
Dataset and Benchmark
Primary area: datasets and benchmarks

Abstract: General SVG modeling remains challenging due to fragmented datasets, limited transferability of methods across tasks, and the difficulty of handling structural complexity. In response, we leverage the strong transfer and generalization capabilities of multimodal large language models (MLLMs) to achieve unified modeling for SVG understanding, editing, and generation. We present the InternSVG family, an integrated data–benchmark–model suite. At its core is SAgoge, the largest and most comprehensive multimodal dataset for SVG tasks, encompassing both static graphics and dynamic animations. It covers icons, long-sequence illustrations, scientific diagrams, and dynamic animations, supporting tasks of varied difficulty levels and providing deeper hierarchies with richer attributes compared to previous datasets. Based on this resource, we introduce SArena, a companion benchmark with comprehensive task definitions and standardized evaluation that aligns with the domains and difficulty spectrum covered by SAgoge. Building on these foundations, we propose InternSVG, a unified MLLM for SVG understanding, editing, and generation with SVG-specific special tokens, subword-based embedding initialization, and a two-stage training strategy that progresses from short static SVGs to long-sequence illustrations and complex animations. This unified formulation induces positive transfer and improves overall performance. Experiments on \benchset and prior benchmark confirm that InternSVG achieves substantial gains and consistently outperforms leading open and proprietary counterparts.

Published as a conference paper at ICLR 2026
INTERNSVG: TOWARDS UNIFIED SVG TASKS WITH MULTIMODAL LARGE LANGUAGE MODELS
Haomin Wang 1.2-
Jinhui Yin 22.
Qi Wei
Wenguang Zeng
Lixin Gu²
Shenglong Ye
Zhangwei Gao 1.
Yaohui Wang
Yanting Zhang Yuanqi Li"
Yanwen Guo Wenhai Wang'
Kai Chen
Yu Qiao'
Hongjie Zhangat
Shanghai Jiao Tong University Shanghai AI Laboratory
NNanjngg University
Donghua University 5 The Chinese University of Hong Kong
Project Page: https: htttps://m//attt http://////mmm///////////// git.:oo...ittooooooooooo thub.
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InternSVG
Understanding
Editing
Generction
PSAggge
SAnena
DVD Desor grlen
Level Level
SSS Teat-re SANE
Icons
SVG
Blastrotionss lans
High-Lend
8
Ang
and in premeiaa Pfaer
Chemistry
Animationn
(a) Diverse SVG Samples
(b) Unified Modeling for SVG Understanding, Editing, and Generation
Figure I: Overview of our InternSVG family. SAgoge provides large-scale and diverse SVG sam- ples across multiple domains. SArena enables comprehensive assessment of existing MILMs on SVG tasks. InternSVG supports unified modeling for SVG understanding, editing, and generation.
ABSTRACT
General SVG modeling remains challenging due to fragmented datasets, limited transferability of methods across tasks, and the difficulty of handling structural complexity. In response, we leverage the strong transfer and generalization capa- bilities of multimodal large language models (MLLMs) to achieve unified mod- eling for SVG understanding, editing, and generation. We present the InternSVG family, an integrated bana-benchmark-modee suite. At its core is SAgoge, the largest and most comprehensive multimodal dataset for SVG tasks, encompassing both static graphics and dynamic animations. It covers icons, long-sequence illus- trations, scientific diagrams, and dynamic animations, supporting tasks of varied difficulty levels and providing deeper hierarchies with richer attributes compared previous datasets. Based on this resource, we introduce SArena, a companion benchmark with comprehensive task definitions and standardized evaluation that aligns with the domains and difficulty spectrum covered by SAgoge. Building on these foundations, we propose InternSVG, a unified MLLM for SVG understand- ing, editing, and generation with SVG-spccific special tokens, subword-based em- bedding initialization, and a two-stage training strategy that progresses from short static SVGs to senueenee illustrations and complex animations. This unified formulation induces positive transfer and improves overall performance. Experi- ments on Srena and prior benchmark confirm that InternSVG achieves substan- tial gains and consistently outperforms leading open and proprietary counterparts.
Equual Contribution. Corresponding Author.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Scalable Vector Graphics (SVG) is an XML-based standard for 2D graphics that offers compact stor- age, fine-grained editability, and resolutioniindppendee either as stand-alone format or as an embedded component within other documents, while also supporting interactivity and animation. Additionally, it interoperates smoothly with CSS, the DOM, and JavaScript in web contexts. These properties have led to broad adoption in web design, scientific visualization, and computer-aides design. Nevertheless, enabling machines to understand, generate, and edit SVGs remains challenging due to the methodological complexity involved, the scarcity of large-scale and high-quality training corpora, and the requirement for models that generalize across tasks rather than specializing in isolation.
rendering across displays. It can function
Existing resources and methods face three major limitations. First, current datasets provide limited support for unified modeling of SVG tasks. Most datasets and benchmarks target a single task, such as semantic understanding in -GP-Bench (Qiu et al.] 2024), instruction- -based editing in SVGEd- itBench (Nishina & Matsui 2024), or Text-to-SVG and Image-to-SWe
generation in CVlorSVG-
100K (Chen & Pan| 2025), SVG-Stack (Rodriguez et al.) 2025). ) and MMSVG (Yang et al.) 20256). This narrow scope leads to fragmented supervision and evaluation, which in turn limits transfer across understanding, editing, and generation. Second, existing datasets are limited in both scale and diversity. UniSVG (Li et al. (025) focuses on unified SVG understanding and generation, but it contains only about 525k samples. SVGenius (Chen et al.) 2023) provides a comprehensive bench- mark spanning understanding, editing, and generation, while it contains only about 2, 2,400 queries, making it suitable for evaluation rather than large-scale training. More broadly, current datasets con- centrate on common static images such as icons and illustrations, while paying insufficient attention to SVGs with specific applications in professional domains. Third, existing methods commonly lack transferability and generalization. Optimization- based and differentiable rasterization pipelines scale poorly and lack semantic reasoning (Li et al., 2020; Ma et al.) (022). Although some ap- proaches (Jain et al. 2023 Xing al. 2024) employ diffusion models to improve visual fidelity, the resulting SVGs often suffer from limited editability and weak detail-awand control. Recently, LLM--assd methods (Xing et al., (025 Rodriguez et al. 2025 Yang et al.) (025b) have achieved significant progress in Text-to-SVG and Image-to-SVG generation, but they struggle to general- ize long sequences and complex SVG content, making it difficult ensure generation quality. Moreover, these methods largely overlook tasks related to SVG understanding and editing.
To overcome these limitations, we pro- pose the InternSVG family, an integrated hata-benccmark-mme
Table I: Comparison of SVG datasets and benchmarks.
suite for unified SVG modeling. Specifically, we introduce SAgoge, a unified multimodal dataset that jointly supports SVG understanding, editing, and generation, covering both static graphics and dynamic ani- mations. SAgoge combines Internet-sourced and synthetic SVG data across icons, illustrations, chemical structural formulas, and animations, totaling about 16 million training samples. Comparison of SAgoge and other datasets is shown in Table [ To the best of our knowledge, SAgoge is the largest multimodal SVG dataset to date, with the broadest task coverage and the most comprehensive range of difficulty. It includes two understanding tasks, ten editing tasks, and four generation tasks, covering semantic understanding, icon editing and generation, Long-sequence illustration generation, animation generation, and the generation of scientific diagrams. To enable rigorous and comparable assessment, we propose SArena, companion benchmark that standardizes tasks and metrics for understanding, editing, and generation, with sub-benchmarks covering icons, illustrations, chemical structural formulas, and animations. Together, SAgoge and SArena provide the scale, diversity, full task coverage, and standardized measurement needed to move beyond fragmented evaluations and enable systematic investigation of general SVG modeling in a unified setting.
Name
Inderdtnding
Editing
Goneration
Multi-hom
Aoumpls
Dalinns
SVG.Suck MMSVG
2.2M
281
SYGX Uisvv
100
5251
(aad
16M
Trachnarke
SGP Beach VGReech
4.3k
SVCamu
2.7k
SArrea (Cunn)
Building SAgoge and the accompanying SArena for systematic evaluation, we introduce In- ternSVG, a unified MLLM for SVG understanding, editing, and generation. InternSVG augments a pretrained vision-language backbone with SVG-specific tokenization, introducing compact special
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tokens for tags, attributes, and coordinates to reduce sequence length while retaining geometric and hierarchical structure. These tokens are initialized with a subword-bas basdd strategy that anchors them in the pretrained embedding space, stabilizing early training and accelerating convergence. Train- ing adopts a two-stage strategy that progresses from short static SVGs to longer illustrations and complex animations. Through extensive experiments, we demonstrate that unified modeling can ef- fectively improve performance across understanding, editing, and generation tasks. Comprehensive evaluations further show that our InternSVG surpasses both open-source and proprietary models on SArena and previous benchmarks. For example, on the SArena-Icon benchmark, InternSVG sur- passes Claude Sonnee-,,, the strongest proprietary baseline on SVG tasks, by about 11% higher acc in understanding tasks, 34% higher PSNR in editing tasks, 56% lower FID in Text-to-SVG tasks, and 22% higher SSIM in Image-to-SVG tasks.
In summary, our contributions are below:
(1) We construct SAgoge, the largest and most comprehensive multimodal SVG dataset to date, encompassing static graphics and animations with over 16 million training samples. To enable rigorous and comparable evaluation, we further establish SArena, a companion benchmark that standardizes tasks and metrics across SVG understanding, editing, and generation.
(2) We propose InternSVG, unified MILM for SVG understanding, editing, and generation. introduces SVG-specific tokenization with llbword-initializedd special tokens and adopts two-stage training strategy to support effective cross-task generalization.
(3) We conduct extensive experiments to demonstrate the benefits of unified modeling. The results on SArena and prior benchmarks show that our InternSVG outperforms traditional approaches as well as general purpose open-source and proprietary models.
RELATED WORKS
2.1 SVG DATASETS AND BENCHMARKS
Most existing SVG datasets and benchmarks are limited in task coverage data type and remain too small for effective model training, leading to fragmented evaluations and limited insights into generalization across tasks and complexity. SGP-Bench (Qiu al. 2024) evaluates semantic com- prehension and consistency in symbolic graphics programs. SVGEdiBeenc (Nishina & Matsui 2024) and its extension V2 (Nishina & Matsui 2025) focus narrowly on instruction- based SVG edit- ing measured by low-level syntactic metrics. On the generative side, SVG- Stack (Rodriguez et al., 2025), SVGX (Xing et al. 2025), MMSVG (Yang et al. 2025b), and Color So-orSVG-100K (Chen & Pan 2025) address Text-to-SVC and Image-to-SVGi generation, while VGBench Zou et al. (2024) and UnGSVG (Li et al., 2025) jointly evaluate understanding and generation. DeepSVG (Carlier et al.) 2020) introduces dataset of 100K SVG icons and explores generation, interpolation, and latent- space animation static and limited animated graphics, but lacks rich editing instructions and image-conditioned generation. SVGenius (Chen al. (225)) introduces comprehensive bench- mark covering understanding, editing, and generation with systematic complexity levels and multi- dimensional metrics, but it includes only about 2,400 queries, making it sufficient for evaluation yet inadequate for training. In contrast, our SAgoge is substantially larger and more diverse, en- compassing both static graphics and SVG animations. It unifies SVG understanding, editing, and generation, and with approximately 16M task samples, its scale and diversity enable robust model training and comprehensive evaluation across the full spectrum of SVG tasks, which effectively address the coverage and scalability limitations of prior datasets.
2.2 SVG MODELING METHODS
Early research on SVG modeling treated vector graphics as sequences of geometric primitives and relied on specialized generative architectures trained on limited domains (Ha Eck 2017 Lopes et al.] 2019 Frans et al.] 2022 Vinker et al. 2022 Hu et al. 2024). Approaches such as DeepSVG (Carler et 2020 used hierarchical VAES with Transformer decoders to generate icons, while optimization-B based methods like DifTVG (Li et al. 2020) and LIVE (Ma et al. 2022) applied differentiable rasterization to align SVG primitives with target images iteratively. Although these techniques captured low-level structure, they lacked semantic understanding, struggled with
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complex compositions, and were computationally costly, often producing redundant unsimplified paths. More recent works have explored diffusion-basee pipelines, such as VectorFusion (Jain et al. 2023) and SVGDreamer (Xing et al. 2024), which refine vector renderings via Text-to-Image diffu- sion combined with SVG constraints. These approaches improve visual fidelity but remain limited generation and offer only restricted controllability and task diversity. The emergence of large language models (LLMs) (Achiam et al.) [223] Yang et al., 2025a OpenAl (025) has shifted SVG modeling toward code-centric paradigms that leverage the textual nature SVG. Methods such as Star Vector [Rodriguez et al. 2025), OmniSVG (Yang et al. 2025b), LLM4SVG (Xing et al. 2025 and SVGBuilder (Chen Pan (025) integrate visual encoders with text decoders to generate SVGs from textual or visual inputs, achieving notable advances in Text-to-SVG and Image-to-SVG syn- thesis. However, these approaches are fragmented across isolated tasks, with limited mechanisms to handle the increasing complexity of SVG content. Different from prior approaches, InternSVG is unified MLLM for SVG understanding. editing, and generation. It capable of understanding both the semantic and structural information of SVG code, editing graphics based user instructions, and generating SVGs from textual or visual prompts.
DATASET AND BENCHMARK
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SVG
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Processed SVG
adjccut mage latara
cos lnd slack Cleam
Figure 2: Overview of the dataset construction pipeline. Raw SVGs are gathered from the web and a custom synthesis pipeline, then normalized to a 128 128 canvas and simplified to shorten code. The rendered images or videos, processed SVG code, and handcrafted prompts are fed to an MILMM to synthesize high-quality training samples for understanding, editing, and generation.
3.1 SAGOGE DATASET
We introduce SAgoge, a large-scale and comprehensive dataset for SVG tasks with more than 16 million training samples spanning icons, illustrations, chemical structures, and animations. The construction pipeline illustrated in Figure 2
3.1.1 TASK DEFINITION
SAgoge is a comprehensive resource that supports unified modeling of SVG understanding, editing, and generation. We define fine-grained task suite spanning diverse vector-graphic categories to enable consistent training and evaluation. Understanding comprises SVG description and multiple- choice question answering. Editing for icons contains ten subtasks organized by difficulty, includ- ing eight low-level operations (color editing, stroke addition, translation, scaling, rotation, flipping,
transparency adjustment, and cropping) and two high-level tasks (semantic color editing and style transfer). Generation encompasses TeXt-to-SVG and Image-to-SVG for icons, illustrations, and chemical structures, as well as Text-to-SANI and Video-to- SANN for animations. Appendix C.3.3 presents examples of tasks in SAgoge.
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3.1.2 DATASET CREATION PIPELINE
Data Source. SAgoge integrates Internet-sourced and synthetic SVGs. Icons and subset of il- lustrations and animations are collected from public repositories such as Iconfont, SVGRepo, and OpenClipart. Chemical structures the Open Babel toolkit. Due to the scareity of open-source SVGs for illustrations and animations, build dedicated data synthesis pipeline expand coverage in these domains. Implementation details are provided in Appendix
generated by converting PubChem SDF files to SVG with
and data statistics of SAgoge are summarized in Table 24
Normalize and Simplify. To improve training efficiency and reduce complexity, all SVGs nor- malized to a 128 x 128 viewBox. Because collected files are often verbose and LLMs operate under limited context lengths, we further simplify the code by removing nonessential metadata, comments, and redundant declarations while preserving the core geometric primitives, semantic information, and hierarchical group structures. This normalization and simplification shorten token sequences, improve consistency, and make the corpus better suited for scalable training and evaluation.
Construction of Understanding and Generation Data. We employ distinct annotation strategies tailored to different data modalities to ensure high-quality. We first render SVGs into raster im- ages or videos, then utilize specific MLLMs with carefully designed prompts. For understanding tasks, we feed rasterized images to MILMs such as InternVI.3 (Zhu et al. 2025) (train set) or GPT- 4o (Hurst et al.) 2024) (test set) to obtain fine-grained geometric and semantic descriptions. For generation tasks, icons and illustrations
annotated by providing rasterized images to MLLM for captioning. For chemical data, we directly use the IUPAC names or common chemical names re- trieved from PubChem textual instructions. For animation data, each SVG animation is converted into an MP4 video and annotated by Gemini 2.0 Flash (Google DeepMind 2024).
Construction of Editing Data. The editing dataset consists of triplets (instruction, original SVG, edited SVG) generated across three categories: simple editing, color editing, and style transfer. Simple editing operations are applied via template-based SVG code modifications and verified by Qwen2.5-VL-72 (Bai et al., 2025). Color editing uses code-level replacement from a 147-color palette with MILLM-generater CLIP (similarity > 0.8) and synthesizes instructions from paired images and captions. Qwen2.5-VL. 72B annotates the train set, while GPT-4o is used for the test set with human verification. Prompt templates for dataset construction are provided in Appendix C.4.I
instructions. Style transfer pairs calegory-consister SVGs using
3.2 SARENA BENCHMARK
To enable systematic evaluation across SVG understanding, editing, and generation, introduce SArena, benchmark that aligns with the domains and difficulty spectrum covered by SAgoge and provides standardized tasks and metrics. SArena includes 4 sub-benchmarks, i.e., icons, illustra- tions, chemical structures, and animation. To ensure reliability and fairness of evaluation, all SVG samples used in SArena are carefully curated through a combination of automated preprocessing and manual screening, with low-quality, corrupted, and semantically ambiguous files removed. Data statistics of SArena are shown Appendix C.2
3.2.1 EVALUATION METRICS
Understanding. We evaluate code-level comprehension with four-option multiple-choice QA. The model receives only the SVG code and must infer the answer from its semantics and structure, and performance is reported as accuracy.
Editing. The model edits the original SVG code to follow textual instructions. Performance evaluated on rendered outputs using DINO score (Oquab et al. (023), Structural Similarity Index (SSIM) Waan et al. 2004). Learned Perceptual Image Patch Similarity (LPIPS) (Zhang et al. 2018), and Peak Signal-to-NN Noise Ratio (PSNR).
Text-to-SVG. The model synthesizes SVGs from textual instructions. Quality is evaluated with FID (Theis et al. 2015) and FID-CLIP (Wu et al., 2023) for distributional and semantic fidelity, CLIP-T2I (Radford et al. 2021 Hessel et al. 2021 for teXt-SVG alignment and CLIP-I2I for visual similarity.
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Orge
Large Language
MLP Projector
Takenizer and Embedder
(b) Length Distribution
Fioel Unshuffle
diploey weber area
Vision Encoder
Instruction
SVG *
Yrak
SVG
Imput Imags Video
Overview of InternSVG
Training Loss Curves
Figure 3: (a) Overall architecuture of InternSVG. (b) Distribution of the number of tokens per SVG before and after adding customized special tokens in the tokenizer. (c) Comparison of training loss curves between subword-based embedding initialization and random initialization.
Image-to-SVG. The model receives reference image and textual instructions and generates the corresponding SVG. Evaluation mirrors the editing task and uses DINO score, SSIM, LPIPS, and PSNR measure visual similarity between the rendered SVG and the ground-truth image.
Text-to-SANI. The model generates SVG animations from textual instructions. Evaluation uses FVD (Unterthiner al. 2018) for distributional consistency and ViCLIP- -based CLIPScores (Wang) et al. 2023). with CLIP-T2V quantifying text-animation alignment and CLIP-V2V measuring vi- sual similarity reference videos.
Video-to-SANI. In this task the model generates SVG animations conditioned on an input video and textual instructions, enabling cross-modal animation generation. Evaluation follows the Image-to- SVG protocol and uses DINO, SSIM, LPIPS, and PSNR computed between rendered SVG frames and reference frames on sampled timestamps, with scores averaged to produce the final result.
In addition to the task-specifi metrics, we also account for cases where the model generates syn- tactically incorrect incomplete SVGs that cannot be rendered. Any unrenderable SVG replaced with pure black image or video as penalty during metric computation, ensuring that the evaluation fairly reflects both the quality of valid outputs and the model's ss robustness in generation.
METHOD
4.1 MODEL ARCHITECTURE
As illustrated in Figure InternSVG follows the "ViT-MLP-LLM" paradigm (Liu et al. 2023 using Vit-rnVTT-30M Chen al. 2024) the vision encoder and Qwen2.5-7B (Qwen Team 2025) as the language model. We further design SVG-specific special tokens and introduce a tailored embedding initialization strategy to incorporate SVG content effectively.
Special tokens. We design 55 tag tokens and 42 attribute tokens based on SVG grammar. Tag tokens cover core structural elements such as svg, path, and circle, as well as animation elements and waaimaaaerraaaoffoomom
like animate. an inimateMotioo geometric attributes (e.g., viewBox, ex, cy, d) and animation-related attributes (e.g., dur, from, reunatCount). To represent numerical values, we add integer tokens from -128 to 128 and 100 decimal tokens from . to .9. This fine-graine ome-grained number representation allows accurate modeling of geometry while keeping the tokenization compact. As shown in Figure Bb).. these special tokens substantially reduce sequence length compared with the original tokenizer, improving representation efficiency and alleviating computational burden.
Attribute tokens include common
to. repeat Count),

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Embedding initialization. Instead of randomly initializing the SVG-specific special tokens, adopt subword-based strategy to ensure semantic coherence and stable training. Each token is decomposed into subwords using the pretrained tokenizer, and the embeddings of these subwords are averaged to form the token embedding. Formally, let a new special token nnw be decomposed into subwords (2,,82....5 8n ), with corresponding embeddings (es Ozg. C.,. The initialized embedding er is computed as:
Σ CS.
Clace
This strategy preserves the semantic prior from the original vocabulary and accelerates adaptation to SVG tokens. As shown in Figure (). it reduces initial loss and accelerates convergence, demon- strating its effectiveness in stabilizing early training and enhancing overall efficiency.
4.2 TWO-STAGE TRAINING STRATEGY
The design of our two-stage training strategy is motivated by the inherent imbalance in SVG corpora. Icons are easy to collect in large quantities and have simpler structures, while illustrations are more difficult to obtain, less abundant, and usually longer and more complex. This imbalance scale and complexity makes unified training challenging. To address this issue, we adopt curriculum-style approach that progresses from simple to complex samples.
Initially, training is conducted only on the Icon and Chemistry datasets, which contain shorter and simpler SVGs. This stage covers SVG description, all editing tasks, and two generative tasks, namely Text-to-SVG and Image-to-SVG. and allows the model to build basic representation and generation abilities. Once the model has reached preliminary convergence, training is expanded to all datasets and tasks, including longer and more diverse SVGs. To ensure balanced learning, the Icon and Chemistry data are resampled to match the proportions of the other datasets. This pro- gressive strategy enables the model to acquire SVG knowledge gradually and stably while reducing imbalance caused by heterogeneous dataset scales and complexities.
Table 2: Comparison of SVG understanding, editing, and generation SArena-Icon. We use up- percase bold initials to denote submetrics with Overall, Color, Geometry, Quantity, and Semantics.
Model
Undeestanding
Editing
CLIPTH
TVG
Imnge-to-5VC LIISS
SooeStop
27-788
17:199
30.994
20993
1.1L
VacocFaice
16:314
17.38L
22,992
70.305
33k
25,612
353322
26319
69.75
1524
Dalva
(1507
0.927
0/97
23.614
17K.
LIVE
0.913
0.945
0.044
15.419
1SL
language
Qwe125.VL-7B
52.8
00.3
50.4
0.009
0.192
24.781
15.454
21.538
71,384
24P
0L781
0
0,318
6.534
281
59.5
79.1
59.3
562
61.3
0,921
0.761
0.170
20.015
23
14.305
21,897
71.450
269
0512
0.557
0.361
7.220
256
Mlann-MMooecc
64.7
81.
52.0
472
613
0.65
0.5.0
1.100
48
09.944
14003
6.325
23.310
253515
255
11565
0,96
0.119
$122
255
634
67.4
651
616
61A
0391
0,819
0.154
41
1110
15915
93375
23.846
73.681
375
0337
0.511
0.346
7.531
372
65.1
86-
11.0
858
519
0.95
0.848
01116
40.53
17.580
10.596
23.855
71.13
352
0.90
0.544
0.319
7.503
334
GPT-4
71.0
0.98
0.068
55:255
15.175
24:67
246
0.316
8.435
73.0
0.817
0.113
75.215
Chidk-Somcc
75.1
9LS
$2.4
0.779
0,071
57.595
15:84
4.201
25.411
90.579
444
0.915
0.655
0.266
9.555
54)
StrWizzer35
0371
0.633
0.366
13.595
451
LLMES i
21.039
3.611
19448
70.225
706
0.748
0.472
0/409
5.325
485
OrniSVC in
18.
11.318
71,679
743831
1.7%
03591
0.756
0.156
13:669
7.4k
InsaSVVSS 5S
85.1 930
85.8
6L9
99.7
0.989
0952
0.86
77.331
8715
1.876
23.916
80.91
1.3%
0U98)
0,811
6.127
18.156
1.3L
18.1
19.1
19.736
16216
12.415
.1.05
10.332
00.04
10.055
14.63)
5
EXPERIMENTS
5.1 EXPERIMENTAL SETUP
InternSVG builds on a pretrained InternVL3-8B model and adopts a two-stage training strategy. In stage one, we train on the Icon and Chemistry datasets that contain shorter and simpler SVGs. Icon provides 1.0M samples for understanding, 1.0M for editing, and 5.6M for generation, and Chemistry provides 2.0M generation samples. In stage two, we expand to all domains and tasks, using 350K understanding, 500K editing, and 1.IM generation samples from Icon, 1.0M generation samples from Illustration, 1.0M from Chemistry, and 120K from Animation. Optimization uses AdamW
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Table 3: Comparison of SVG generation results on SArena Dlliaa--llsttattion.
TeX
Image-to-SVG
Model
FID J
F CLIP-T21
CLIP-1E1 1 Tokens
DINO
SSIM
LPIPS
PSNR
Tokens
Traditional SVG methods
VectorFusion
28.198
15.661
22.638
71.220
33k
SVGDreamer
32,410
23.427
21.026
69.775
132k
DiIVG
0,870
0.886
0.138
21,605
17k
LIVE
0,963
0.948
0.078
29.055
18k
VTracer
0,965
0.879
0.093
21.754
10k
Large language models
Qwen2.5.VL. VB
37.903
28.455
18,069
61,928
756
0.739
0.513
0.413
7.732
1.2k
V1.-88
36.736
25.682
18.493
61,964
493
0.772
0.569
0.397
8.542
716
Liama-4MMavricc
30.835
14.831
21.872
67.366
551
0.839
0.644
0.340
608
Qwen2.5-VL-72B
29.521
18.407
20.923
63,349
527
0.808
0.628
0.363
9.900
886
InternV1.3-78B
30.457
19.195
20.577
64,826
454
0.830
0.638
0.348
9.985
514
GPT-4o
28.124
14,150
23.637
70.696
473
0.850
0.663
0.327
10.723
484
Gemini-2.5-Flash
28.865
8.894
24.800
74.7%
1.2k
0.829
0.516
0.359
9.091
1.8k
Claude-Sonnets
27.294
7.640
23.094
74.525
0.305
1.3k
LLM4SVG
48.704
29.568
15,468
62.933
0.713
0.494
0.413
6.221
476
OmniSVG 3B
42,756
22.885
16.861
64,815
4.5k
0.797
0.656
0.330
10.433
6.7k
IntemSVG 8B
22.397
5.141
21.116
74.662
8.1k
0.924
0.716
0.188
14.644
7.7k
+4.897
+2.499
-3,684
.0.144
+0.023
+0.046
+0.117
+2,913
Table 4: Comparison of SVG generation results on SArena-Chemiste and AArena-Animatio
Test-te-SVG
Chemistry: Imsg-to-SVG
Animstian:: Test-toSANi
Animation:
Vides-to-AAI
Model
1
PID-C 1
CLIPER1
DINO
LPPSS
PSNK
FVD
CLIP-TIV
CLAPAVIV
DINO
LPPSS
PSNK
Qwenz 5 VL.18
56,243
73,695
51.114
0.769
0.468
0.214
7.50
214.379
19:115
50,640
0.767
01716
0.273
11.758
ImiraVL33.08
09.61
0.988
0.798
0.171
141 470
22.434
67.615
0.754
01315
Qwen25 VL-728
32.307
44.500
03.051
0.846
0.647
0.315
12.105
151.682
20.376
59.454
0.54
0.721
0.261
11.331
0.704
GP9-4
0.900
0.391
0.541
23.708
21.777
03.897
0934
0.817
0.155
15.530
151 963
22:230
66.35 334
0.667
0.701
0.257
12015
Chadi-Sanree44
31:257
15,340
73
0.57
0.871
0.132
34,779
74.179
0.867
1L768
0.340
13.19
StuVVettor
0.341
Incm5VV 8B
9.994
0,877
93.031
0.994
0,873
0.158
17.722
09.474
22.572
73.162
0.876
0.254
0.237
14.168
+11.274
+14.365
-15.25
+0.017
+0.003
40005
+0.170 770
-14498
-0.009
-0.00
+0.005
with learning rate of 2 x 10 A800 GPUs with a per-device batch size of 1.
4 in stage and 1 x 10 in stage two, running on 96 NVIDIA
5.2 QUANTITATIVE EVALUATIONS
To validate the effectiveness of our SAgoge dataset and InternSVG, we evaluate multiple models on the SArena benchmark. The evaluation covers open-source MLLMs, proprietary systems, tra- ditional SVG generation methods, and LLM-based approaches, including LLM4SVG (Xing et al.) 2025), Vector (Rodriguez et al. 2025), and OmniSVG (Yang al., 2025b). The complete list of evaluated models with their parameter scales is given in Appendix B.
As shown in Table 2 we systematically evaluate various models on SArena-Icon across understand- ing, editing, and generation tasks, Our InternSVG outperforms the second-best model by 8 points in Overall accuracy. In editing, InternSVG achieves the best scores across DINO, SSIM, LPIPS, and PSNR, yielding higher visual fidelity and geometric consistency. For Text-to-SVG. it achieves the best FID, FID-C, and CLIP-I2I among all methods, while its CLIP-T2I is comparable to that of Claude-Sonnet-4. In Image-to-SVG, optimization-base InternSVG achieves comparable visual similarity while producing much more compact code about 1.3k tokens per SVG, roughly one fourteenth of LIVE's token count, and it unifies understanding, editing, and generation within a single model.
methods remain strong on similarity, yet
As shown in Table 3 on to-SVG, with CLIPScores comparable to Gemini-2.5 2.5-laah,, the best-perf proprietary system, and outperforms both general MILMs and -LM-based methods in Image-to-SVG. On SArena- Chemistry, where common or IUPAC names are used as prompts, existing MLLMs perform poorly on Text-to-SVG due limited training scientific graphics, while InternS VG achieves the best
our method achieves the best FID and FID-C in Text-
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Figure 4: Visualization of SVG samples generated by InternSVG.
sults on both tasks with large margin in Text-to-SVG ( On SArena- InternSVG follows a protocol of sampling frames from video imput and surpasses all open-source MLLMs, reaching performance close to proprictary models on both Text-to-SAn and Video-to-SANI. Com- plete evaluation results are provided in Appendix [B.2]
To fully validate the effectiveness of SAgoge and InternSVG, we further evaluated the performance of InternSVG on established benchmarks, including SGP-Bench (Qiu al. 2024), SVG-Stack (Ro-
driguez et al.) 2025), and UniSVG (Li et al., 2025). Notably, InternS VG achieves the best score SGP-Bench, surpassing proprietary models like Claude-Sonnet-4, and dominates UniSVG with Final Score of 0.826. On SVG-Stack, InternSVG achieves superior Dino, LPIPS. and MSE scores with comparable SSIM, using only 1.2k tokens compared to Star 3.7k-5.3k. The results demonstrate that the SAgoge dataset and InternSVG framework provide significant improvements across diverse SVG tasks, proving their efficacy extends well beyond the specific data distribution of SArena. Additional results reported in Appendix B.3 B.4 and B.5
5.3 QUALITATIVE VISUALIZATION
Figure presents qualitative examples of SVGs generated by InternSVG. The results show that our InternSVG can produce diverse and visually appealing graphics with clear structures and semanti- cally rich content. More qualitative visualization results of InternSVG and comparisons with other models can be found in Appendix
B.9 B.9
5.4 ABLATION STUDIES
Table Ablation results Generation (G), Understanding (U), and Editing (E) under different task combinations.
Text-to-SVG
Imago-to-SVG
Editing
Overall
FID
FID-C
CLIP-T2I
CLIP-121
DINO
SSIM 1
LPIPS
PSNR
DINO 1
SSIM 1
LPIPS
PSNR
G
13,548
7,848
21,073
73.016
0.807
0.547
0.387
6,360
E
0.943
0881
0.125
42.131
G+U
75.1
12.88
6,750
21,070
73.310
0.813
0.563
0.382
7,042
13,842
21.099
0.808
6,897
0.969
GrUie
12.387
21.198
73,630
0.814
0.379
7.095
0.977
0.906
Benefits of Unified SVG Modeling, To validate the effectiveness of unified SVG modeling in improving model performance, we sample 100K SVGs from the Icon dataset and construct a multi- task training set. The set includes 100K samples for multiple-choice QA and 100K samples for SVG description in the understanding task, 100K samples for semantic color editing in the editing task, and 100K samples each for text-to-SVG and image-to-SVG in the generation task. As shown in Table 5 we compare single-task, two-task, and three-task training configurations. The results show that multi-task joint training leads to consistent performance improvements and achieves the best results across the evaluation metrics of all tasks. Overall, three-task joint training yields significant gains in most metrics, which confirms that unified SVG modeling facilitates cross-task knowledge

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Published as a conference paper at ICLR 2026
transfer, enables the learning of richer structural and semantic representations, and improves the generalization ability of the model.
Table 6: Ablation results on the effects of the two-stage training strategy.
Stratogy
Illosration:: Test-t-SVG
Illsttatioo::
Image-io-SV
Animation:: Test-in-ANI
Aninstion: Vidro-in-SANI
FID-C
CLIP-TI1 CLIР-221
PSNR
CLIP-TIV
CLIPAYIV
SSIM
Choostnge
25.671
19.217
404000
0.830
0.503
22.291
69.091
0:667
0733
0.245
Two stge
22,397
5141 21,116
74,62
6,924
0,716
0.188
14661
99474
22,573
73,062
0,576
0.754
0,277
14.168
Comparison of One-stage and Two-stage Training. To verify the effectiveness of the two-stage training strategy, we merge the data from both stages into single-stage training scheme as a base- line. As demonstrated Table [ the two-stage model achieves substantial advantages on both il- lustration and animation generation. Especially for the long- sequenced illustration generation task, the two-stage training strategy exhibits remarkable improvements, with FID-C reduced from 25.67 to 5.14 and DINO increased from 0.830 to 0.924. More comparisons can be found in Appendix (B.7
Benefits of SVG-specific special tokens and subword-bar based embedding initialization. To assess the contribution of these two components, we sampled 50% of the icon, illustration, and chemistry training data used in stage 2 and trained three model variants: Model Raw (no special tokens), Model T (special tokens with random initialization), and Model T+E (special tokens with subword- based initialization). As shown in Table 7 Model T+E consistently delivers the best performance across both benchmarks, demonstrating that combining special tokens with subword-based embed- ding initialization provides the largest overall gains. Furthermore, models that incorporate special tokens (Model and Model T+E) exhibit substantially higher success rates on illustration gener- ation, which involves longer SVG sequences. This trend aligns with the token-compression effect shown in Figure introducing SVG-specific tokens reduces sequence length, stabilizes training, and ultimately improves task reliability.
Table Ablation studies on the effects of SVG-specific special tokens and subword- based embed- ding initialization. SR denotes Success Rate.
Tex
Image-to-SVG
Model
Overall
SR
FID J.
FID-C J CLIP-T2I + CLIP-121
SR
DINO 1 SSIM LPIPS
PSNR t
SArena-Icon
22.767
84.30%
0.381
79.7
97.62%
11.922
4.578
23.275
77.374
89.59%
0.919
0.741
0.166
14.342
T E
80.8
98.42%
11.599
4,500
23.348
77.541
95.64 %
0.937
0.795
0.139
15.837
SArena- Illustration
Raw
57.22%
68.301
26.108
18.870
66.222
30.08%
0.735
0.331
0.412
6.053
69.02%
53,080
22.358
19.174
66.469
57.37%
0.807
0.448
0.307
9.424
78.81 %
42,817
17.438
19.194
67.379
75.31 %
0.862
0.578
0,257
11,810
CONCLUSIONS
In this work, we introduce the InternSVG family, a unified data-benchharrkmodee suite for scalable vector graphics. It integrates the large-scale and diverse SAgoge, the comprehensive benchmark SArena, and the unified MLLM InternSVG. SAgoge encompasses broad spectrum of SVG tasks with varied difficulty levels, ranging from static icons and scientific diagrams to long-sequence il- lustrations and dynamic animations. SArena provides standardized evaluation framework that assesses both mainstream MLLMs and traditional SVG generation approaches, enabling consis- tent comparison across tasks and domains. Through extensive experiments, we demonstrate that this unified formulation enhances SVG understanding. editing, and generation, yielding consistent improvements across domains and tasks. Our study highlights the importance of unified SVG mod- eling, and we hope it can serve as an insight for future research on vector- grator- reasoning and multimodal intelligence.
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REPRODUCIBILITY STATEMENT
All the results presented in this paper can be reproduced with the resources provided. We describe the model architecture and training settings in Section 4 and Section [ The implementation of
SVG-specific tokens is provided in Appendix AA] Details of the dataset, including construction and
preprocessing, are provided in Section 3.1 Appendix C.3 and Appendix C.4
ETHICS STATEMENT
Our work does not involve any sensitive personal data. All dataset elements were collected from open-source websites and publicly available sources, and we have taken care to exclude any content that could be discriminatory violate copyright terms. We do not anticipate any adverse societal impact from our methods or datasets.
ACKNOWLEDGEMENTS
This work was supported by the Shanghai Artificial Intelligence Laboratory, the Shanghai Commit- tee of Science and Technology (No. 22YF1461500), and the National Natural Science Foundation of China (No. 62206046).
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A IMPLEMENTATION DETAILS OF INTERNSVG
In this section, we provide the detailed implementation of the SVG-specific tokens used in In- ternSVG. All SVGs undergo normalization to a canonical 128 x 128 coordinate system. This standardization reduces spurious coordinate drift and decreases computational burden for learning absolute magnitudes, thereby mitigating coordinate hallucination and enhancing cross-task consis- tency.
We extend the base vocabulary with SVG-specific tokens that compress lengthy character sequences. As demonstrated in Table the extended vocabulary incorporates 55 tag tokens covering structural and animation elements, including opening and closing forms such as <svg, </ </svg>, <path, <animate and <ani Semimmtteii TraatTrans form. Additionally, include 42 attribute tokens encom- passing geometry, styling, and animation controls with embedded quotes, such as viewBox=", width=", x=", and fill=".
For compact numerical representation, we introduce 247 integer tokens spanning -22 to 128, plus 100 two-decimal and 10 one-decimal fractional tokens. This fine- aune-grined numeric inventory enables precise geometric specification while maintaining short sequences, yielding substantial reductions sequence length and computational requirements.
New token parameters undergo initialization, preserving the base tokenizer's semantic knowledge. Each added token's embedding equals the average of its constituent subword embeddings when seg- mented by the original tokenizer. This procedure applies uniformly across all token types. Follow- ing vocabulary expansion, training proceeds end-to-end without parameter freezing. The subword- derived initialization stabilizes optimization, accelerates convergence, and aligns new symbols with adjacent embedding regions, improving grammatical decoding and numerical fidelity across all tasks.
B RESULTS OF SARENA
B. EVALUATED MODELS
Proprictary Models. We include several leading proprietary MILMMs in our evaluation, namely
GPT-4o (Hurst et al. (024), Gemini-2.5-Flash (Comanici et al., 2025). Claude-Sonnet-3.7 (An-)
thropic 2024). -caude-Sonnet-4 (Anthropic 2025), and Grok (xAl 2025).
Open-Source Models. We further benchmark diverse set of open-source MLLMs to provide comprehensive comparison, including
Llama-3.22 1129BBBB/BBBBB (Dubey al.)
2024]. Llcout (Meta AI, 2025b). Llama-4-Maverick (Meta Al (025a). Qwen2.5-V VL
7B/32B/72B (Bai et al. 2025). Keye-VL-8B
et (025c)) GLM-4.1 V-9B (Hong al. 2025),
GLM-4.5V (Zeng al. [025), Gemma-3- Gemma-3-I2BB77B (Team et al., 2025a). Kimi- VL-ABBL-A3B (Team
ct al. 2025b), Step3-321B (StepFun Team 2025). InternVI.3 series (Zhu et al. 2025 and In-
tern V3.55 series (Wang et | 2025).
Traditional SVG Generation Methods. We also evaluate a range of traditional SVG generation
methods, including Vectorfusion (Jain et al. 2003). IconShop (Wu et al. 2023). SVGDreamer (Xing et al. 2024), DiffVG eLi al. 2020). LIVE (Ma et al. 2022 and VTracer (Vision Cortex 2023).
L.M-based SVG Generation Methods. To thoroughly validate the effectiveness of InternSVG. we compare it against representative LLM-based SVG generation approaches on the SArena bench- mark. Specifically, we include LLM4SVG (Xing et al. (025). StarVector (Rodriguez et al. 2025),
and OmniSVG (Yang et al. 2025b), which exemplify current efforts in leveraging large language models for SVG generation.
B.2 COMPLETE EVALUATION RESULTS ON SARENA
Table 2
B 14 and 15 present the complete evaluation results on the SArena benchmark
across all four domains and tasks.
As shown in Tables 9 standing and Editing tasks. For the Understanding task, model obtains the best results across
I0 and II InternVGG achieves substantial improvements on both Under-
15

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Published as a conference paper at ICLR 2026
Table 8: SVG-specific token vocabulary in InterSSVG.
(a) Tag tokens
Category
Tokens
Root
<svg, </svg>, <defs, </defs>, <use, </use> ,/>
Grouping
<g, </g>
<path, </path>, <rect, </rect>, <circle, </circle>, <ellipse, </ellipse>, <line, </line>, <polyline, </palyline>, <polygen, </polygon>
Shapes
<text, </text>, <tspan, </tspan>, <textPath, </textPath>
Text
<linearGradient, </LinearGradients, cradialGradient, </radialGradientb, <stop, </stop>
Gradients
Clipping
<clipPath, </clipPath>, <rask, </mask>
<filter, </filter>, <feGaussianklur,
Filters
c/EeGaussianBlur>, <ieColorMatrix, </feColorMatrix>,
<feComposite, </feComposite>, <feBlend, </feBlend>
<animate, </animate>, <animateMotion, s/animateMotion>, sanimateTransfozm, </ //animateTransfomm
Animation
(h) Attribute tokens
Category
Tokens
width-", " height-", viewBox=", x-", y=", xl-", yl-", x2-", y2-", cx-", cy=", x-", rx-", ry=", d-", points="
Geometry
fill-", stroke-", strcke-width-", streee-1 woook-llnnece linecap-" stroke-linefoins,, stroke-mite mitekiimtterrimtt"=, fill-rule=", epacity="
Styling
Transform
transform="
Text
font--izz==, font-famill familfmm""=", text-anchor="
gradientUnits=", gradientTransfommey offset=",
Gradients
stop-color="
Animation
begin=", dur=", repeatCount=",, from=", to=", rotate=", path="
Identifiers
id=", class=", elip-path="
Table 9: Comparison of SVG understanding and editing performance SArena-Icon.
Understanding
Editing
Model
Overall
Color
Geometry
Quantity
Semantic
DINO
SSIM
LPIPS
PSNR
Tokens
Qwen2.5-VL-7B
52.8
69.3
50.4
34.9
56.4
0.909
0.728
0.192
25.402
1.0k
InternVLL3-BB
59.5
79.1
59.3
38.2
61.3
0.921
0.761
0.170
29.615
1.2k
Gemma-3-279
59.5
82.2
67.6
43.6
44.7
0.942
0.815
0.113
54.200
1.3k
Qwen2.5. VLL-32B VL-32B
65.5
82.8
65.5
47.7
66.1
0.933
0,782
0,148
37.737
1.0k
Llama-4-Scout
57.5
82.4
57.0
41.6
49.0
0.949
0.825
0.138
34,070
1.3k
Llave-k-Mavcrick
64.7
87.5
62.0
47.2
62.3
0.966
0.870
0.109
46,944
1.3k
VL-722
63.4
82.4
65.1
44.6
61.6
0.961
0.849
0.124
41.006
1.2k
Intern VL3
65.3
86.4
71.0
48.8
54,9
0.958
0.848
0.116
40.533
1.2k
GPT-4o
71.0
88.2
78.5
47.5
69.6
0.968
0.887
0.088
55,255
1.2k
Gemini-2.5-Flash
73.0
90.1
81.9
53.0
67.2
0.942
0.815
0.113
54,200
1.3k
Claude-Sonnete
77.1
91.5
82.4
53.8
80.6
0.979
0.915
0.071
$7.595
1.3k
InternSVG
85.1
93.0
85.8
61.9
99.7
0.989
0.952
0.036
77.331
1.4k
all metrics, surpassing the second- best Claude-4-Somin by 8 points Overall accuracy. Especially on the Semantic subtask, the score reaches 99.7, indicating that InternVVG demonstrates strong capability in comprehending the semantics encoded in SVG code. The simple-level editing tasks primarily assess proficiency in SVG syntax, including the use of nested groups with the transform
16

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Published as a conference paper at ICLR 2026
attribute to carry out translation, rotation, and flipping operations. After training on SAgoge, InternSVG effectively acquires these syntactic skills and achieves perfect scores in several subtasks. On the hard-level editing tasks, InternSVG attains the best performance in semantic-level color editing, consistent with the Understanding results and further confirming its strong semantic com- prehension. However, in the more challenging style transfer task, the model still lags slightly behind Claude-Sonnet-4, leaving room for improvement.
Table 10: Comparison of SVG editing performance across 8 simple subtasks on SArena-Icon.
Modkl
level Coloe Editing
Cropping
Flipping
Retution
DINO
SSIM
LPIPS
PSNR
DINO
SSIM
LPPPS
PSNR
DENO
SSIM
LPIPS
PSNR
DIND 1
SSIM
LPIPS 1 PSNR
Qwi2 5-V1-78
0.958
0.592
0.061
25. 133
0.820
0.673
0.270
10.067
0.852
0.636
0.313
9.685
0.919
0,805
0.152
47.813
InimmVL.1-88
0.93
0.003
0.055
75.568
0.584
0.705
0.257
10:271
0.704
0.250
23.198
0.157
0999
0,992
0.007
65.47 475
0.881
0.261
0.195
11.326
0.905
0.704
0.241
13.358
0.385
0.897
0.246
21.118
Gomma-3-227 3-278
1.000
1,000
0.003
90.057
0.885
0.619
0.202
14.116
0.955
0.962
0.008
06.554
0.991
0.945
0.041
85.314
1 X00
0.995
0.005
91,006
0.389
0.732
0.235
0916
0.769
0.195
23.892
0.708
0.262
Qwer2.5-V0 -121-128
0.967
0.014
0.044
88.40
18.400
0.003
0.657
0.906
9.062
0.919
0.807
0.154
55.634
0.985
0.959
0.004
90. 566
Liama
0.909
0.925
0.009
57.067
0.879
0.652
0.283
9.134
0.901
0.755
0.206
21.027
0.74
0.926
0.011
80.043
Mimer--Mmeercc
0.988
0.996
0.005
874
0.903
0.672
0.901
9.404
0.955
0.114
0.074
96.565
0.989
0,967
0.074
86.
Qwer2.5 VL 72B
0.995
0.985
0.005
91.542
0,509
0.658
0.307
9.174
0.948
0.874
0.890
52,671
0.992
0,949
0.445
82.266
0.995
0:587
0.008
0.682
0.299
0.129
10,065
Imte 241B
0.983
0.955
0.021
91,202
0,504
0.763
0.225
11.763
0.8%6
0.754
0.103
30,961
0.901
0.785
0.188
39.965
GPT-46
0.995
0:987
0.007
0.913
0.688
0,300
9.556
0.994
0.916
0.015
87,44
0.995
0,986
0.010
94.845
Gemini-
1.000
1,000
9.761
99.057
0,585
0.619
0.297
14.116
0.995
0
0.008
96.554
0.991
0.945
0.41
85.314
Clande Sonner 4
1.000
1,000
0.000
100,0000
0,528
0.696
0.291
9.625
0.944
0.943
0.055
73.786
0.999
0
0,006
95.666
InceSVV
1,000
1,000
0,000
100,000
1,000
1.000
0.000
100.000
0.987
99.692
Model
Scaling
Adiing
Stook:
Trandation
Truspeecciss
DING
5SIM
LPIPS
PSNB
DINO
SSIM
LPIPS
PSNR
DENO 1
SSIM
LPIPS
PSNR
DINO
SSIM
LPIPS
PSNR 1
5-V12-9
0.902
0.653
0.362
0.738
0.295
0.923
0.684
0.231
12.403
0.933
0.791
0.150
35.353
0.916
0.705
0.223
27.231
0.982
0.954
0.026
67.912
3ateraV1.3.5-8
0.90
0,310
0.234
16.638
0.996
0.721
0.162
20.350
0.917
0.600
0.276
12.508
0.989
0.967
0.044
59.219
G-27
0.43
0.546
0.100
67.250
0.565
0.857
0.116
40.216
0,962
0.896
0.045
63.705
0.333
0.667
0.141
63.444
Inte/V13..55
0.930
0.695
0.236
14.118
0.549
0.769
0.138
27.933
0.947
0.746
0.223
52,946
0.992
0,968
0.024
63.078
0.917
0.673
0.236
0.739
0.139
0.748
0.191
Llama-4-Scout
0.925
0.005
0.225
13.008
0.560
0.840
0.104
38.360
0.926
0.686
0.251
18.387
0.63
0,957
0/028
66.299
Llama 4 Maverick
0.927
0.776
0.194
23,361
0,970
0.856
0.073
52.249
0.956
0.741
0.226
31,710
0.995
0
0.006
94.957
VLL
0.901
0.678
0.267
11,492
0.965
0.875
0.106
46.055
0.704
0.256
72.101
IatenVL_3-788
0.931
0.695
0.235
12.792
0.947
0.790
0.145
37.317
0.957
0.331
0.134
46.221
0.992
0.984
0.015
68.573
IntemVL3335 2410
(.919
0.661
0.245
11,857
0.948
0.762
0.136
27.335
0.928
0.750
0.160
25,850
0.089
64.399
GPT-4o
0.947
0.811
0.163
0,966
0.864
0.093
48.913
0.982
0.928
0.060
72,016
0.977
0.014
85.619
Gemini 2.5 Flash
0.943
0.545
0.100
07.28
260
0,565
0.857
0.116
40.216
0.962
0.8%6
0.045
$2.705
0.883
0.687
0.141
63.444
Clasle-Somnes4
0,953
0.533
0.138
50.330
0.907
0.055
51:913
97.535
InteSVG 8B
0.999
1,000
0,000
95.635
1,000
1.000
0.000
99.488
1,000
1.000
0.000
100,000
1,000
1,000
0.000
99.968
Table 11: Comparison of SVG editing performance across 2 hard subtasks on SArena-Icon.
Semantic-level Color Editing
Style Transfer
Model
DINO
SSIM t LPIPS + PSNR t
DINO 1
SSIM t
LPIPS 4
PSNR t
Qwen2.5-VL-7
0.919
0.768
0.166
23.902
0.889
0.658
0.193
11.940
Intern VL3-8B
0.903
0.728
0.184
22.071
0.917
0.728
0.158
13.457
Comma-3-278
0.981
0.920
0.072
53.068
0.869
0.591
0.210
12.174
Qwen2.5-VL-32B
0.926
0.769
0.158
28.290
0.910
0.723
0.162
14.283
Llama-4-Scout
0.964
0.860
0.120
27.852
0.963
0.848
0.119
15.417
Llavei-M
0.975
0.891
0.099
41.222
0.969
0.855
0.105
16.765
Qwen2.5-VL-72B
0.975
0.888
0.100
42.759
0.957
0.836
0.113
16.771
Intern VL3-78B
0.955
0.857
0.105
27.033
0.912
0.705
0.175
13,429
GPT-4o
0.972
0.912
0.073
54.651
0.952
0.819
0.117
18.173
Gemini-2.5-Flash
0.981
0.920
0.072
53.068
0.869
0.591
0.210
12.174
44ude--oonet-4
0.991
0.944
0.050
56.741
0.976
0.867
0.097
18.374
InternSVG 8B
0.996
0.959
0.041
69.875
0.952
0.808
0.139
18.100
The complete results for both Text-to-SVG and Image-to-SVG tasks on icons and illustrations are InternSVG consistently outperforms prior SVG generation ap-
reported in Table 12 and Table 13 proaches across all major metrics. Compared with large-scale soun-s models such as Llama- 4-Maverick and GLM-4.5V. it achieves lower FID, FID-C, and higher CLIP-I2I scores, with only a slight gap on CLIP-T2I. For Image-to-SVe InternSVG achieves the best performance across all metrics, highlighting its strength in visual fidelity and semantic consistency.
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Published as a conference paper at ICLR 2026
Table 12: Comparison of SVG generation performance on SArena-Icon (Text-to-SVG and Image- to-SVG).
Text-oo-VV
Imuge-to-SVG
Model
FID
FID-C CLIP-TZ1
CLIP-121
SR
Tokens
DINO
SSIM
LPIPS PSNR
SR
Tokens
Traditional SVG
methods
IconShop
32.288
17.919
20.894
70.922
86.51%
1.5k
VectorFusion
16,594
17.394
22.992
70,308
100.0%
33k
SVGDreamer
26.612
33.312
20.329
69.975
100.0%
132k
DirvG
0.869
0.927
0.097
23.614
100.0%
17k
LIVE
0.973
0.986
0.024
35.419
100.0%
18k
VTrwer
0.054
21.748
100.0%
4.4k
Large language models
IJamn-3.1-8B
19.428
11:247
21,863
71.859
97.54%
280
Qwen2.5-VL.77
24.781
15.454
21.538
71.384
90.72%
249
0.781
0.506
0.378
6.534
86.35%
281
Keyo-VL-8B
21.961
14.393
21.557
71.167
93.71%
227
0.801
0.531
0.368
6,939
90.95%
286
G..M4.IVV99
22.684
10.447
22,562
73.197
94.11%
269
0.820
0.539
0.345
7.329
84.12%
289
InterVVL3-BB
23.061
14.303
21.897
71.450
93.96%
269
0.812
0.557
0.361
7.220
94.76%
256
17,357
7.128
21 888
75.005
91.72%
1.0k
0.852
0.618
0.291
8.737
49%
692
Llama-3.2-111
186
14.345
21.711
71.485
86, 18%
261
0.759
0.467
0.389
5,908
81 46%
216
Gemma-3-128
17.137
10.409
22.023
71.622
98.40%
290
0.821
0.576
0.352
7.632
95.29%
360
11te
18.996
13:224
22.066
71.493
98,
227
0.825
0.562
0.359
7.343
96.77%
216
LmtemmLL3..114B
15.897
5.985
22.351
75,912
92.82%
1.0k
0.607
0.308
8.455
93.00%
832
Kimi-VL-A3B
30.807
16.996
21.439
70,536
86. 18%
228
0.562
0.362
7.179
92.20%
245
16.779
5.595
23.017
77.455
90.74%
802
0.908
0.706
0.196
12.747
90.25%
1.0k
Gemma-3-277 3-27B
15.145
9.303
22.526
73.277
99.37%
249
0,826
0.595
0.354
7,833
99.50%
267
IntemVL3 5-30
16.307
5.843
22.483
76.397
92.08%
961
0.883
0.653
0.270
9.636
96.37%
783
Qwen2.5 VL--2BB
20.043
10.393
22,783
73.228
93. 98%9
317
0.836
0.562
0.357
7,503
96.92%
309
1333raVL3-388B
18.014
11.042
22.795
73.077
97.21% %
251
0.829
0.549
0.351
7.305
93.40%
230
14,558
5,218
22,546
76.487
94.46%
1.0k
0.861
0.611
0.320
8,393
96.24%
1.0k
Grok-3
21.967
8.694
24.122
76.797
84.17%
346
Llama-3.1-70B
18.032
8,300
22.747
73.876
96.08%
255
1.mma-3.1-405B
16.794
8,390
22.822
73,920
96.87%
236
D3epSeek-V3
24.990
8,803
23.790
76.470
82.27%
251
GPT-4o
15.178
6.763
24.617
77.742
99.47%
246
0.874
0.616
0.316
8.435
08.00%
231
Gemini-2.5-Fash
16.720
5.208
24.658
78.218
97.345%
451
0.876
0.587
0,316
8.324
90.50%
533
3..nee7
14.383
3.499
25. 294
80,786
99.15%
417
0,909
0.647
0.290
9.259
98.34%8
389
Clande-Soonet44
15,840
4.291
25.421
80,579
99.57%
444
0
0.665
0.276
9.855
99.63%
541
Lluma 3.2.993.
19.309
8.550
22.841
74.006
94.89
249
0.757
0.437
0.377
5.777
67.75%
192
Llama-4-Scout
17.908
9.382
22,849
73.563
96.03%
256
0.844
0.582
0.346
7.736
97.61%
246
Ilaaaa--Maverick
14.931
6.526
23.570
75.816
98. 12%
265
0.863
0.596
0.329
8.027
97.75%
255
GLM-4.5V
16.641
5.093
24.450
78.349
97.32%
372
0.872
0.627
0.315
8,666
98.24%
322
Stop3-311B
20.061
9.706
23.055
74.184
86.73%
308
0.834
0.555
0.340
7,516
86.53%
301
Qwen2.5.V1.-20
15.948
9.875
22.946
73.681
98.50%
275
0.584
0.346
7,834
372
InteraVL3-78BB
17.580
10.596
22.805
73.123
98.05%
0.584
0.339
7,802
98.95%
234
11.265
4,426
22.534
76.807
96, 99%99%
991
0.882
0.642
0.291
9.187
96.15%
914
SturVector 8B
0.871
0.623
0.206
13.595
72.5156
951
LLM4SVG 7B
21.939
8.611
19:458
70.726
90.95%
705
0,748
0.472
0.409
5,375
95.53%
485
OmniSVG 3B
28.292
11.318
21.679
74.831
99. 68%
1.7k
0.894
0.756
0.186
12.669
99.97%
2.4k
InterSVGG 8B
8.715
1,876
23.916
80.911
97.24%
1.0k
0.949
0.811
0.127
18.226
94.45%
1.3k
Table 14 reports the complete evaluation results on AArena-Chemistry for both Text-to-SVG and Image-to-SVG tasks. In this benchmark, common names and IUPAC names of chemical com- pounds are used as instructions to guide models generate structural formulas. The results indicate that most existing MLLMs perform poorly in Text-to-SVG generation due to their limited training exposure to scientific graphics. For example, Qwen2.5-Ve V-VB-2BB obtains FID scores around 32 and relatively low CLIPP-11 scores. In contrast, InternSVG achieves the lowest FID (9.974) and FID-C (0.877) and the highest CLIP--III (93,931), surpassing all baselines by a large margin. On Image- to-SVG, InternSVG also outperforms both open-source and proprietary models, achieving the best results across DINO (0.994), SSIM (0.873), and PSNR (17.722). These results highlight the strong capability of Intern in handling domain-specific scientific graphics.
For animation generation, InternSVG achieves strong performance on both Text-to-SANI and Video- to-SANI tasks according Table 15] In the Text-to-SANI task, it attains the lowest FVD of 99.4444 and ranks second on CLIP-T2V and CLIP-V2V, only slightly behind Claude-Sonnet-4. In the Video-to-SANI task, VVG demonstrates performance comparable to even surpassing that
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Published as a conference paper at ICLR 2026
Table 13: Comparison of SVG generation performance on SArena (Text-to-SVG and Image-to-SVG).
Text-to-VVG
Imuge-to-SVG
Model
FID
FID-C CLIP-TZ1
CLIP-121
SR
Tokens DINO
SSIM
LPIPS PSNR
SR
Tokens
Traditional SVG methods
Vestorf'usion
28.198
15.661
22.638
71.220
100.0%
33k
SVGDreamer
32,410
23.427
21.026
69.775
100.0%
132k
DifVG
0.870
0.886
0.138
21,605
100.0%
17k
LIVE
0.963
0.948
0.078
29.055
100.0%
18k
VTracer
0.965
0.879
0.093
21.754
100.0%
10k
Large lunguage models
Qwen2.5-VL-7B
37.903
28.455
18,069
61.928
88.81%
756
0.513
0.413
7.732
83.46%
1.2k
V1.388
36.736
25.682
18.493
61.964
91.45%
493
0.772
0.569
0.397
8.542
92.70%
716
70.857
35.776
18.095
63.357
51.52%
3.6k
0.721
0.306
0.410
5,283
48.43%
2.5k
Ln1mmVL3.3-148
65,967
34,912
18.131
63.496
51.97%
3.5k
0.722
0.296
0.414
5,130
47.53%
2.8k
Gemma-3-270
27.838
13.766
21.486
67.255
98, 80%
613
0.824
0.617
0.379
9.920
98.45%
764
IntemVL3..-300
68.438
33.285
18.354
63,910
54.57%
3.8k
0.739
0.331
0.404
5.778
52.37%
3.0k
Qwen2.5-V1-32B
32.115
17.804
19:773
64.555
94.70%
779
0.816
0,591
0.382
9.297
95.25%
828
IntemVL.3..188
42,172
21.556
18.221
65.511
78.21
4.3k
0.755
0.393
0.400
6.540
64.07%
3.8k
Llama-4-Scout
35.489
18.647
20.299
64.182
91.80%
524
0.807
0.599
0.360
9.549
92, 80%
574
Mlava--Mavericc
30.835
14.831
21.872
67.366
97.30%
551
0.839
0.644
0.340
10.469
98.20%
608
Qwen2.5.LL-7BB
29.521
18.407
20.923
65.349
98.60%
527
0.628
0.363
9.900
InterVVL33788
30.457
19.195
20:577
64.826
97.40%
454
0.830
0.638
0.348
9.985
99.35%
$14
IntemmV1....241BB
43,339
23.061
18.191
65.689
75.86%
2.9k
0.792
0.480
0.378
8.093
77.26%
3.1k
GPT-4o
28.124
14.150
23.637
70.696
99.75%
473
0,850
0.663
0.327
10.723
99.35%
484
Gemini-2.--lash
28.865
8.894
24.800
74.79
96.50%
1.2k
0.829
0,516
0.359
9.091
77.41%
1.8k
Mlaude-Soneet 4
27.294
7.640
23.094
74.525
98.85%
1.0k
0.901
0.670
0,305
11,731
99.25%
1.3k
Statoector 8B
0.650
0.070
0.447
1.990
8.050%
2.6k
LLM4SVG 78
48.704
29.568
15.468
62,933
79.36%
1.2k
0.713
0.494
0.413
6.221
96.60%
476
OmniSVG 3B
42.756
22.885
16.861
64.815
99.75%
4.5k
0.797
0.656
0.330
10.433
100.0%
6.7k
InterSVVG88 8B
22.397
5.141
21.116
74.662
100.0%
8.1k
0.924
0.716
0.188
14.644
90.01%
7.7k
Table 14: Comparison of SVG generation performance on SArena-Chemistty (Text-to-SVG and Image-to-SVG).
Text-to-VVG
Image-to-SVC
Model
FID
FID-C
CLIP-12I
SR
Tokens
DINO f
SSIM f
LPIPS
PSNR
t
SR
Tokens
Qwen2.5 VL--BB
56.248
73.698
51.814
65.60%
907
0.769
0.468
0.274
7,501
60.97%
996
InterVLL3-8B
33.613
61.675
56.856
90.81%
910
0.865
0.783
0,203
13,840
96.27%
805
3-27B
29.937
49.967
60.776
92.07%
776
0.887
0.823
0.190
14,959
98.63%
683
Qwen2.5
53.047
56.431
58.428
68.309
1.2k
0.821
0.570
0.225
10.005
67.03%
900
Llama-4-Scout
33.781
46.584
62.522
87.95%
849
0.866
0.734
0.205
12.984
89.08%
624
Llave---Maverick
26.844
31.924
69.643
93.97%
747
0.908
0.798
0.173
14.977
93.27%
687
Qwen2.5- V-222B
32.307
44.540
63.931
90,34%
620
0.846
0.647
0,215
12.106
76.76%
716
IntemVLL3-78B
29.216
40.080
65,969
92.07%
698
0.911
0,813
0.177
15.375
96.24%
545
GPT-4o
24.505
19.297
76.599
97.37%
640
0.920
0.791
0.174
14.673
92.21%
533
Gemini-2.5- Flash
27.708
21.777
75.897
92.07%
1.4k
0.934
0.817
0.155
15.539
94.44%
Claude-Sonnet-4
21,252
15.240
78.308
97.90%
1.2k
0.957
0.871
0.132
17.554
99.37%
956
Stur Vector 8B
0.977
0.841
0.147
17.419
96.70%
1.2k
InterSVV SB
9.974
0.877
93,931
99.93 %
981
0.994
0.873
0.138
17.722
100.0%
931
of Claude-Sonnet-4. Its SSIM score is only lower by 0.006, while it achieves higher results on all other metrics.
B.3 RESULTS ON SGP-BENCH
To further validate the effectiveness of SAgoge in enhancing model capabilities for SVG modeling, we conduct comparative experiments on SGP-Bench (Qiu et al.) 2024). a benchmark specifically designed to evaluate semantic and structural understanding of symbolic graphic programs (e.g.. SVGs). As shown in Table 16 our InternSVG achieves an overall accuracy of 72.3%, substan- tially outperforming existing open-source models. In particular, it surpasses the second-bes open- source MLLM, GLM-4.5V. by 6.2 percentage points. When compared with proprictary systems, InternSVG also demonstrates advantages, achieving 1.3 percentage points higher overall accuracy
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Published as a conference paper at ICLR 2026
Table 15: Comparison of SVG generation performance on SArena-Animation (Text-to-AANI and Video-to-SANI)
TexI
Video-to-SANI LPIPS
Model
FVD J. CLIP-TI CLIP-V2V
SR
Tokens
Qwen2.5
214.379
19.118
94.25%
0.273
11,758
95.63%
423
IntemVV1.38B
74.01%
9.883
82.54%
Gemma-3.278
12.290
98.41%
Owen2.5-VI.332p
98.21%
0.696
11.417
96.83%
Llama-4-Soout
167.932
21.014
62.929
100.0%
505
0.831
0.742
0.259
12.427
99.80%
Llama-A-Maverick
141.470
22.304
67.615
100.0%
563
0.841
0.754
0.246
12.858
100.0%
Qwen2.5.V1.:22
151.682
20.376
59.454
97.62%
433
0.834
0.721
0.261
11,931
98.61
402
InternV1.3778R
169.159
20.263
60 896
97.42%
409
0.828
0.704
0.264
11,336
97.82%
385
GPT-4o
155.393
22,808
70.608
80%
404
0.860
0.743
0.250
12,260
99.80%
400
Gemini-2.5-Plash
151,983
22.239
66.554
93.85%
986
0.847
0.701
0.257
12,015
91.87%
917
Claude Sonnet-4
169.484
24.070
74.179
100.0%
9007
0.867
0.760
0.240
13.189
99.80%
866
Intern& VG 8B
99.474
22.572
73.162
99.01%
812
0.876
0.754
0.237
14.168
97.82%
888
Table 16: Comparison of SVG understanding performance SGP-Bench.
Model
Semanties t
Count
Color t
Shape
Reasoning
Overall
Demma-1.1-2BB
32.1
33.3
25.0
35.6
28.7
31.7
InternLM2.5-7B
27.3
31.7
59.8
51.5
28.2
42.1
Keye-VIL-80
41.4
47.5
71.4
54.9
40.6
52.2
GLM-4. V-9B
41.6
55.6
79.1
61.5
40.0
57.1
InternV1.3-8B
33.7
46.5
69.8
59.1
36.1
50.6
Gemma-3-122
24.8
30.8
47.2
25.7
22.8
30.5
DeepSeek-Coder SrepSeek-Coder-V22-163 V2-16B
30.9
37.9
63.7
54.8
26.8
45.1
In.--nV1.3-14B
38.2
52.9
74.4
54.1
41.7
52.9
Kimi-VL-A3B-2500
31.1
41.5
67.0
47.4
32.4
44.9
Gemma-3-27B
36.7
51.4
76.3
62.1
39.4
54.7
Qwen2.5-VL-32B
40.0
55.7
76.3
61.2
43.9
56.5
Intern VL.3-3BB
40.8
58.7
82.2
63.6
43.9
59.1
GPT-4o
45.9
56.8
87.3
75.2
50.4
64.8
Gemini-2.5- 2.5-Flash
53.8
57.8
88.1
75.6
55.5
67.6
-laude-Sonnet-4
55.9
67.6
89.5
79.0
58.9
71.5
GLM-4.5V
47.3
63.7
87.3
72.3
55.8
66.1
@wen2.5-VL-72B
40.2
55.1
80.1
62.0
41.1
57.1
JnternVL3-78B
41.0
59.1
84.0
65.2
47.0
60.3
Step3-321B-A333
35.9
54.0
82.8
63.2
38.6
56.5
InterSVVG
54.6
70.7
85.5
82.4
57.5
72.3
than Claude-Sonnet-4, while also leading on the Count and Shape subtasks. The strong performance on demonstrates the effectiveness of SAgoge enhancing SVG modeling.
B.4 RESULTS ON SVG-STACK
We evaluate the performance of InternVVG SVG-Stack (Rodriguez al. 2025), ) benchmark that
focuses on the Image-to-SVC task. As shown in Table InternS achieves the best performance Dino, LPIPS, and MSE, while using significantly fewer output tokens compared Star (1.2k vs. 3.7k-5.3k), and obtains comparable SSIM scores.
B.5 RESULTS ON UNISVG
UniSVG (L et al. 2025) is a comprehensive benchmark designed to evaluate both SVG under- standing and generation capabilities. On UniSVG, InternVVG achieves the best performance with
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Published as a conference paper at ICLR 2026
Table 17: Comparison of Image-to-SVC results SVG-Stack.
Method
Dino
LPIPS
SSIM 1 MSE J Tokens
GPT-4V
0.852
0.317
0.711
0.195
443
StarVector IB
0.926
0.149
0.840
0.078
3.7k
StarVector 8B
0.966
0.058
0.947
0.026
5.3k
InternSVG 8B
0.968
0.052
0.916
0.023
1.2k
a Final Score of 0.826, substantially outperforming all baselines. particular, it attains 0.939 ISVGEN score on the Image-to-SVG task, which shows 0.166 improvement over the second- best method, and also obtains the highest TSVGEN score of 0.741 on the Text-to-SVG task. For the SVG understanding task, InternSVG achieves the highest accuracy of 0.625 the Hard set, while demonstrating comparable performance on other metrics. Together with the strong results on SGP-Bench and SVG-Stack, these findings indicate that the benefits of InternSVG are not limited to SArena or any related data source. Instead, they demonstrate that the SAgoge dataset and the InternSVG framework provide robust and broadly generalizable improvements across diverse SVG understanding and generation tasks.
Table 18: Comparison of model performance on UniSVG.
SYGRN
SVGEN
Model Name
Final Score
ISYGEN
TSYGEX
Aucanay
Bet
Low Level
level
Eaye-Ace
SSIM
LPPPS
CLPP Spor
SSIM
LPPPS
CLPP Score
11 12MM4-92-38
0.567
0.565
0.674
0.690
0.592
0.491
0.616
0.772
0.688
0.347
01301
-0:301
01455
Qween25-VLL710
0.614
11304
0.614
01738
0.833
0,538
0.619
0.764
0.612
0.343
01571
0.082
0.550
GPI--V
0.69)
0.557
0.582
01240
0.898
0.620
0.511
0.166
0.212
0.991
01448
0.211
0.530
0.43
0.622
0.755
0.624
LLaMA 3.2-333-3BBB
32-TB-
0.732
0.722
0.378
0.643
0.775
0.617
0.511
0312
0.709
0.990
0,500
0.511
0.735
VL.7B
0.368
0.621
03326
0.390
0.100
1.970
0,919
0.600
0.900
0.808
0.741
0.981
16625
0.364
0.611
B.6 QUANTITATIVE EVALUATION OF SVG CODE QUALITY
Table 19: Comparison of generated SVG code quality SArena-Icon.
Model
Path
Rect
Circle
Polygon
Line
Ellipse
Polyline
Time
GT
2.98
0.35
0.29
0.17
0.10
0.06
0.02
SVGDreamer
256.00
0.00
0.00
0.00
0.00
0.00
0.00
289s
VectorFusion
64.00
0.00
0.00
0.00
0.00
0.00
0.00
71s
IconShop
6.19
0.00
0.00
0.00
0.00
0.00
0.00
5.7s
LLM4SVG
2.81
0.03
0.17
0.00
0.00
0.01
0.00
9.7s
OminiSVG
4.30
0.00
0.00
0.00
0.00
0.00
0.00
12.3s
InternVVG 8B
4.48
0.25
0.20
0.17
0.07
0.02
0.01
6.9s
To complement visual metries, we conduct a quantitative evaluation of the structural quality of generated SVG code. We utilize the SArena-Icon benchmark, which contains 6,013 real human- designed icons, ensure that the analysis reflects the structural properties expected in real design scenarios. Inspired by the complexity metrics proposed in Sygenius (Chen et al.) 2025). we mea- sure path count via XML parsing, assess the conciseness of command structures, and compare the average inference time across models on Text-to-SVC tasks. We also measure the average counts of SVG primitives to evaluate each baseline's structural expressiveness more comprehensively.
As shown in Table 19 InternSVG demonstrates strong performance in structural expressiveness, editability, and inference efficiency. InternSVG natively supports the full set of SVG primitives, avoiding the heavy Bézier-curve approximations used by optimization-base methods such as SVG- Dreamer (Xing et al. 2024) and VectorFusion (Jain et al. 2023). This primitive- has also been highlighted enabling InternS VG to model diverse geometric structures more naturally and accurately. Further- more, IV produces highly concise outputs, with average of 4.48 paths, compared with
representation essential in recent work such as Star Vector (Rodriguez et al.) (022),,
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Table 20: Comparison of one-stage and two-stage training stralegy across SArena-Icon, SArena- Chemistry, SArena-Illustran and SArena-Animation
(a) Icon & Chemistry: Editing, Text-to-VVG and Image-to-SVG.
Iangetto
Clessstry: Baite-VV
Insog-tu-SV
PSNK
FID-C1
1
CLIPaIT
DINO
LPIPS
FSSR
CLIPI
DINO
LPPPS
PSNR
8992
0.904
8:00
65.211
10.854
34.3%
0510
18351
1190
92.81
0353
6:71
0.39
17665
8.06
17.30
8015
33.96
80911
8811
90.30
0.94
(b) Illustration & Animation: Illustration (Text-to-SVe -vi--tt--VG/// / Image-to- tonge-to-SVGG and Animation (Text-to-SANI Video-Io-SAND.
Middl
Illdration: Text-to-SVG
Illutration:
Imnge-t--VVG
Animation: Tex--t-AANI
Animation: Video-ss-SANI
FID FID-C. CLIPTT2E CLIP-E
INIO
SSIM
LPPPS
PSNK
1 CLIP-TIV
CLIP-VIV
DINO
SSIM
LPIPS
PSNR
One sage
68.644
25.071
19:217
66.000
0,300
0.503
0.78
10.34
101433
22,291
09.993
0.867
0.735
0.245
13420
Two 0to-stae
14,168
256.00 for SVGDreamer and 64.00 for VectorFusion, whose fragmented paths are often difficult to modify. The cleaner, semantically coherent structures generated by InternSVG align more closely with practical design workflows. In addition, optimization-baser methods require tens to hundreds of seconds per sample (e.g., 289 s for SVGDreamer and 71 for VectorFusion), whereas InternSVG generates results in only 6.9 seconds average, benefiting from its explicit structural encoding. This yields substantially faster inference without compromising structural quality.
B.7 TWO-STAGE vs. SINGLE-STAGE TRAINING
In this section, we provide a comprehensive analysis of the one-stage and two-stage training strate- gies. As shown in Table 20 for Icon generation, the one-stage model performs relatively well be- cause Icon samples dominate the training data in the single- stage- setting. Nevertheless, the two-stage model achieves comparable results and remains superior on FID, FID-C, and SSIM. For Icon edit- ing as well as Chemistry, Illustration, and Animation generation, the two-stage model consistently outperforms the one-stage baseline. The experimental results further validate that the proposed two-stage training strategy is effective in alleviating data imbalance, and the progressive training scheme from easier to more challenging tasks leads to substantial improvements in SVG modeling performance.
B.8 CROSS-DOMANE GENERALIZATION ANALYSIS
To rigorously quantify the cross-domain generalization strength of InternSVG, we conducted zero- shot cross-domain transfer experiment. Specifically, we trained a variant of InternSVG 8B only on the Icon Image-to-SVG data used in stage 2, and then directly evaluated it on the -Arena-Chemiste Amage-to-SVCC task, without using any Chemistry data during training. The results show that this icon-only variant still achieves substantial improvements over the original Intern VL3-8B across all metrics. As shown in Table 21 the DINO Score increased from 0.865 to 0.930 (7.5%1). SSIM increased from 0.783 to 0.824 (5. 5.2%%)..... LPIPS decreased from 0.203 to 0.6 (23. (23.2%.), and PSNR increased from 13.84 to 16.296 (17.8%1). The comprehensive improvements across all metrics strongly validate that our method and data possess exceptional generaizzation capabilities in cross- domain transfer scenarios.
Table 21: Zero-shot cross-domain transfer performance on SArena-Chemistry.
Model
DINO 1
SSIM 1
LPIPS J
PSNR
SR
Intern VL3-8B 8B
0.865
0.783
0.203
13,840
96.27%
InternSVG 8B w/ icon
0.930
0.824
0.156
16.296
98.61 %
B.9 ADDITIONAL QUALITATIVE VISUALIZATION
In this section, present additional qualitative visualizations of InternS on SVG understanding, editing, and generation tasks. We further compare the generated SVGs with those produced by baseline methods to assess visual quality.
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As illustrated in Figure 5 and for the Text-to-SVG generation tasks on icons and illustrations, VectorFusion and SVGDreamer produce visually appealing results but exhibit poor instruction- following ability and often include large number of irrelevant elements, limiting their practical applicability. Existing general-purposs MILMs typically suffer from structural instability and in- complete semantic representation in SVG generation, making it difficult balance visual quality and semantic alignment. In contrast, LLM4SVG and OmniSVG frequently fail to generate valid outputs or produce results that are entirely inconsistent with the textual description, highlighting the limited generalization ability of current LLM-based approaches for SVG generation. By compari- son, InternSVG consistently generates high-quality and semantically aligned SVGs across diverse scenarios, demonstrating the advantages of its unified modeling paradigm for understanding, edit- ing, and generation. Figure and compare the performance InternSVG with other approaches on the Image-to-SVG task. The results demonstrate that InternSVG not only substantially outper- forms gencral-purpont MLLMs and cxisting LLM-based SVG generation models, but also exceeds LIVE in performance while requiring significantly fewer tokens. In terms of detail preservation, InternS demonstrates superior capability compared with existing approaches.
As shown in Figure 9 a for the generation of chemical structural formulas, existing general- purpose MLLMS generally lack relevant training data and therefore fail to produce correct chemical structures in either task. In addition, compared with StarVector, method demonstrates superior control over fine-grained details of chemical bonds.
Figure 11 and present comparison between InternSVG and general-purpose MLLMs SArena-Animatin Animation. Existing approaches show limited ability in handling vector animations, of- struggling with visual quality and motion control. In contrast, InternSVG demonstrates superior capability, producing animations with higher fidelity and more consistent temporal dynamics.
Figure tasks.
13
14 and
15 present visualizations of InternSVG outputs on SVG understanding and editing
Gemini- -2 R-Pllshh
Ulena 4 Maverick
Qwent s. VL- -728
Text Prompt
Vecherlster
SVaDreomer
GPT 44
Clside 4 Semeet
LLMASV
OmvisVV
Ours
A green cartoon frog face with yellow backeround and circulor shope.
0
camera icon
A blue cloud shape with three vertical white bars.
III
11
white envelops icen inside circle.
blue speech bubble with white lightning balt inside.
Figure 5: Qualitative comparison of Text-to-SVG performance between baselines and In- ternSVG on SArena-Icon. Red cross icons denote cases where the model failed to generate valid SVG output.
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Gemini &.B. Paahh
Liann Meveriot
Gwent 5-VL 728
Image Prompt
bifive
LIVE
GPr.
Cluue Somett
Starvector
LLMAAV
DeeeSVV
Durr
grean pear brewn leaf.
orange pot
A
covered axtrlopping clouds.
pursle calender icon with lange b blccknubeer one.
1
bleck circular gouge gorgulargaage with blue are and a white needle.
yellow cincle with o surprised face. festtrring lange white eyes, brown spebrows, nouth.
0.0
stroight
Figure 6: Qualitative comparison of Image-to-SVG performance between SOTA methods and InternSVG on SArena-Icon. Red cross icons denote cases where the model failed to generate valid SVG output.
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brunch, Published as a conference paper at ICLR 2026
Text Prompt
Vector fatio
avebreamer
Claste 4, Samet
Gemini 2.5 Flash
Liane Maverick
Qwenz 5-VL- V2-728
LLMASVG
OmeisV
GPT
Ours
A siigle male Figur a site von, ruering one bein forwand and The other short
parts, and blue shees The vector art ssea smooth, rounced shopei
with
medunneeigtt fit solid strokes
capeske cherry hap, ng capcakke is connroll placed with belge wnoppor and dark chocokate bose. The vecher
X
postel colore. syrrettiical, cherry spox datriibtte
with ro wablie strolice The composttion
branch, facing forward. buals and roundedl body with loyenal feather shopes. The vec upis dleas includes hepe, and onanpe with flrt lel The composition end
x
strokes, The
creating bulonced
with sealing and migboneee
dep ched in froen-ficing poss, Ie iis eight axinly spaced noyer echescing artword
bleck necitnngl for sungleazes and cerved line for mimutt, The vectir uses siople medium solid strokes
tymmetmiical with the sun centered and raye recieting everty.
rocket in vertical exhoat It Fest unoio white badyy fins, and bike cir caloon wndoww using simple rectonglis
pasa, with
clson, rourded digee, fiss fill emplays white, and bllee with mediue solid strokes
composition centers the rocktct, the
Florse
usward moticn.
One butter flynn frontol pode, with wings sonead kide. The buttorfly features
Syemetricall
edges
shopes ovels
and pattee The vecto- employs medium- weihht strokes and flet composition is cesfonnss with the autterfly ecouyyng noeh of the space. creating 6 belanded foyelt
Figure 7: Qualitative comparison of Text-to-SVG performance between SOTA methods and InternSVG on SArena-llustration. Red cross icons denote cases where the model failed to gen- erate a valid SVG output.
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Imogge Prospt
Dirive
LIVE
GPT -o
Claude 4. Somal
Gemini S.Plesh
Ulaoo 4 Raveridk
Qwent & VL. 728
LLMASVS
Durt
Ohe depper in mid-aap, depicted eids Vew streamk mod body, dorsal fir. us smooth mast of skak, wins mascium sold strolens The composi ue boos leyoned affecct present.
X
+
scave
houdphenes 25 dark anoring only face, and hords, dieale uaes dleas amoots carvas rounded edoa. centered, groy, focing fonwerd and en team is present defie hirx.
side
lng with the other burn foohamen pink body, sureed
vechor emeeth beckut adges,
reanded ata back and centrolly ploced, en the haod nurnad aligtlly cnasling bulamced compotttin.
One fruet fustng with flfffy body and seall ligs light colored eyet and sarved horrs The vector anea blick brown,
bege, peach. makke The composition is symmettoo mool with the sncol loyeul.
composed thrse eteccso cinese for the sery I9 werre trimple for fecill Fartunet vorhor upen cloon and anonge and mudiue ockif ctroker The compeatiin iin cestered. ed, snowmen fr frieddl appearance
black deta
One iirafficin ficing pear, morting dankerbrow browe spote, smal tall and to fiat brown, blas, metiin soli ekes. genfffeis and small blue ground patch around cresting sielie ieperol
Figure 8: Qualitative comparison of Image-to-SVG performance between baselines and In- ternSVG on SArena-lllsstratoon Red cross icons denote cases where the model failed to generate a valid SVG output.
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Gaude 4 S-Smmet
Gemini- 5-Flash
Liama 4 Maverick
QwenZ 5-VL B-VL-728 -728
Text Prompt
44
Ours
A Structural formulas of 1- Methoxy-9, 10-anthraceneddone
A Structural formulas of 1- (Chloromnthyy/raphhhhoeeee
A Structural formules of 2- Phenylpropylamine
A Structural formulos of 3- Phenyl-5 (2 (2-piperidin-1- 1- ylethy]jssxazzole
A Structural formulas of 5- Fluorobenzo(d]P (dwpo//d-o----amiiiieie
A Structural formulas of 2,5-
Dichlerobenzamide
Figure 9: Qualitative comparison of Text-to-SVG performance between baselines and In ternSVG on -Arent-Chemistty.
Image Prampt
Cloude 4- Somet
Gamini 2 5-Plash
Liana 4 Maverick
QuenZ. 5-VL- 728
StarVector
GPT GPT-4o
Ours
Structural formulas of 2,3,4- Trihydroxy-B- 8-
(hydrooymeehyl)oxoorrr carboxylic acid
Structural formulas of Skf-
Structural formuloss of m- Chiorophanylbigunnide
Structural formulas of 4- Butyry/pyridine
A Structural formulss of Heliamine
poo
Structural formulas of 1.6- Pyrenequinone
Figure 10: Qualitative comparison of Image-to-SVG performance between baselines and In- ternSVG on -hheistChemissry.
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sprad wutthe can chows axchet wasd ise creCeEt pork from Salor clad fyin blan die pattrrtewflll
Text Prompt
Sigter
with Light cala- eabilyy dith gnd ceetting fkoenrggfffee officct
Syhningg ionit borkyroudd
GPT-4o
-lamme-4-Semmet
R
I
2 5-Fllsh
Llons-4-Maverick
5-VL-788
Ours
alvte backgroun nighlggt moses cheelr dong meg momffererce Thec chonge trovds sonetmoa pngge other musmuagit
Aguratt bachgrannd dork blee number parttened the keft number zevend gittt groy iine and carva Fliving whis corved sbave Salle sapaa and chnspear fiffhmmc continuca movamant.
Text Prompt
oroand
ploer
arnsticn cantneau mation.
GPT-4o
10
10
10
10
10
Goude-4-Someet
10
10
10
Genini-2 2anini-2,5-fiie 5-Fllsh
Llave---Maverick
Qwen2 5-VL-728
2
Figure 11: Qualitative comparison of Text-to-SANI performance between baselines and In- ternSVG on SArena-Animation
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Axdas arthoshiie backgroed festurer these surficclio hove the centen Pron eliecn diipedeeer,, Repheeeeyi.ttthhiieiee Aethnicaly corterayonn keytuniing in cortonnee
presurt orcik amorged in herteanta ags a lightr dicik th
Text Prompt
GT
GPT-4o
Soume-4-Semeet
Genini-2.5-F
Lloms-4-Maveriee
Owen2.5-VL-728
Curs
ling, paffy dosc vello canutte natictigg nas peaks thalle oray ckis thes pay bunnd live.
The
Witkin dquen frrro ouhit beckywar foa kynbaand hanutal coler seft lameder whentt negerts.cd and deep futiekett buttiee The
Text Prompt
the cloa
GT
Slove-4-Soweet
Gemini-2 2-5Feih2 5-Flesh
-loms-4-Nowerick
5-VL-72B
Ours
Figure 12: Qualitative comparison of Video-to-SANI performance between baselines and In- ternSVG on
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Question
The SVG source is below:
<ovg wiins- http://wwwww......o...o... 2000//00/ width "128" height= vicw8ax==0
128 spath fil- #FBBDC
d-MM22 77 65.73c 7.36
101129-88--88 80-8888.990 18.40-2.73 0-7.46-8.54-1333
1..3344-1-11333.2335 54-11.33.339999 01..33339933--35 9.37-.45
12.6c-5.8 B4 1 224-4,32 11.53-2.33 14.78 3.71 7.5477 14 14.22-12.2 12.82 1,771-3.59 6.9/a22.6 22.6 0:0-11 22.50 22580
02100 14.400 asho, Jeti 28 15.94453 19 115-11.811111111 06-1 9663331-6.11 162--3-41.864 88-19, 752 <>>ggggg>
identily the main object degicted, and list geometric shapes (e.g.. rectangles, cirdles, lines) as well semantic cues
(e.g what the object is its contextt implied function).
Response
The code depicts sleeveless shirt with the folloving properties:
"Geometric Properties**
simple, elongated
rounded neckline and arnholes.
"*Symimery"* is vertically symmetrical, with both sides mirroring each other.
"Unes*": There are two horizontal lines across the lower part of the shirt, creating banded appearance.
Properties.**
"Color": The light with darker blue outline.
*Style** resembles tank top sieeveless athietic shirt, suggesting casual spartswear
"DDsign"" The horbontal lines may indicato design feature seam.
straightforward
design, typical casual
Question
following
height=
<path
2.79-22
3.65--13-26-
23.7
16602
4v55c0
4-4V24c0-2225
4-52:0
3.5813.18.52
3.1
144-4.71.17-2216666
3.61-3554
1.84
12.44-1.46 23.22223.11155
35 6586C7 110-22 80.53 116
116H41
57C30.56
116 22 107.1 22 9514440 2
1.70-4 3.80-4v3.69 1.71 3.80
4v27c0 2.21 1.7 44 4p4-1 70 4-4V50L0
2.22 7.75-4 3 230443389
1.74 3.89 4v35.0 1.79
4444-11777 4-42//
Iil 'ra 1225 d- M5 122- 0-3.50
1.74.3 3.89 4v52:0 2.21 1175 4-94-4444
4-1.79-4 4-4/2000
216-1.79-4-3.88 -79-44-3.99---3388
1.74-3 4w41c0 2.21 1.79 4-4 45-4-11 1.79 4-V4420 2.26 1.79
3.89 4522
41.74
44-5200
8.81 19:57
20h30 70h-30122d855 12c8.53 0 16.03-5 58 67-137777
4 (22-12 12.37 1171-23,.22
15.5-35.5
35.6.00
14.03-15 15,1-53522 15-12-11111111
41-154--225
3.542.47 B-5.44 8-9.05
55-1.71 27-3.35
3.52-5 5.381- 17
7.74-3144
54-4.64 49744 0 1011-044444444
01 77.12 78V2420-2 2--10-22211111 8-4-38994-- 8--4--29-21111 0-3.80 1.74. 34--3.8 4v47c0 2.21-1 70 4-444-4444
4-4-4444444 41 11441177..44111.02222-88-8--4-
4'/x </lg>>
Desembe in detail the semantic and geometric features the SMG code
Response
depicts sleeveless
following properties.
"Geometric Properties.**
"Shape"* The shirt has simple, elongated shape with rounded neckline and armholes.
"Symietrr""*
symmetrical, with mirroring part the shirt, creating appearance.
"Lines"::
horizontal
"Semantic Praperties.**
*Coolor*: The shirt is light blue with darker blue outline.
top "DDssgn**: The horizontal lines may indicate design feature seam.
""Styla"" resembles
shirt, suggesting casual sportswear.
Overall the shirt has straightforward and functional design. typical of casuel or athietic
Figure 13: Visualization of InternSVG outputs SVG understanding.
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Instruction
are precision editing engine. Please change color graphic
Change +10693a to turquose (#40E000)
Low-level Color
Editing
Instruction
You are specialized SVG editing assistant.
Crop the SVG graphic to show only part.
Crop left half the
Cropping
Instruction
You predision SVG editing engine. Please flip the harsantally
vertical
Flipping
Instruction
You are an automated 5VG modification unility. Rotate the SVG graphic around its center, Rotate koske by 90.50 degrees around center
Rotation
Instruction
You specisiced SVG editing assistant. scale this Scale around center
Scaling
Figure 14: Visualization of InternSVG outputs on SVG editing.
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Instruction
Eeit SVG based instruction. Add border the Add thick red outline wieth 2.78
Adding Stroke
Instruction
Edit SVG based the instrucion. Move the SVG graphic in certain direction. move left by 15.33, move down by 16.44
Translation
Instruction
Act SVG code manipulator, Set the graphic effect. transparent jopacity 12).
W
Transparency
W
Instruction
expert code Change eyes' mouth
graphic.
beige.
Semontic-leve Color Editing
Instruction
Rotate the hour hand so it sits halfweyy between and 5; keep the minute hand at cock.
Style Transfer
Figure 15: Visualization of InternSVG outputs on SVG editing.
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B.10 USER STUDY AND EVALUATION DETAILS
We conducted user study to comprehensively evaluate the quality of SAgoge's synthetic annota- tions and the practical efficacy of InternSVG's generation capabilities. A total of 42 valid responses were collected from participants with academic professional backgrounds relevant to our study, including 14.29% researchers, 57.14% PhD students, and 28.57% UI designers. The cohort primar- fields and also included professional UI designers. These qualitative
ily came from computer-relatee evaluations complement the quantitative metrics presented in the main experiments, providing fur- ther validation of data reliability and model performance.
Gedeoc blek
(a) Consistency check and Pairwise Preference Test
(b) Synthetic Data Quality Evaluation
(c) Text-to-SVG / Text-to-SANe SANI Comparison
(d) Image-to-SVe / Video-to-SANI Comparison
Figure 16: Screenshots of user study. (a) Participants judge whether a caption matches the image, and compare two captions to decide which one better describes the content. (b) Participants rate the quality and human-likenese of synthetie SVGs. (c) & (d) Participants compare the outputs of six different models for Text-to-SVG Text-toSAAN and Image-to-SVG / Video-to-SANI tasks.
First, we assessed the quality of our synthetic annotations to ensure they serve reliable supervision for model training. Specifically, we sampled 100 model-generated instructions from our dataset, comprising 40 icons, 40 illustrations, and 20 animations. We designed two types of evaluation tasks for these samples (see Figure 16a): a consistency check and a pairwise preference test.
In the consistency check, participants judged whether the model gederreed instruction is semanti- cally consistent with the rendered image or video. In the pairwise preference test, participants were shown human-authored instruction and model-generrted instruction for the same image (with
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sources blinded) and selected among "A is better", "B is better", "Both good", or "Both bad". As summarized Table 22 the results show significantly high semantic consistency (95,0) 995.0%% overall). In the comparative assessment, participants prefer model-generater instructions in 45.43% of cases, while human-authored instructions are preferred in only 15.67% % cases, and 36.90% of samples considered equally good. Overall, more than 82% of the samples indicate that model- generated annotations are comparable to human annotations in quality.
To more directly assess whether there is noticeable gap in quality or "human-likenes" compared real human-authored SVGs, we additionally conducted a user study on the synthesized illustration and animation data (see Figure 16b). For each participant, we randomly sampled five illustration SVGs and five animation SVGs. presenting both the SVG code and the rendered images. Participants evaluated: (i) whether the image contains any visible defects; (ii) the likelihood that the content is created by human designer (Human-Likeness Score); and (iii) the quality and readability of the SVG code (Code Quality Score). Both scoring metrics (ii) and (iii) were evaluated on a 1-5 scale, with higher scores indicating better quality. The results, shown in Table 23 indicate that for illustrations, 96. 19% of samples are judged to contain no defects, with a Human-Likeness Score of 4.20 and Code Quality Score of 4.31. For animations, 93.81% of samples defect-free, with a Human Linnness Score of 4.07 and a Code Quality Score of 4.23. These findings demonstrate that synthesized data is generally of high quality and closely aligned with human design conventions, validating the reliability our synthetic pipeline.
considered
Table 22: Human evaluation results on synthetic data annotation quality (N=42, Total Samples=504).
Semantic Consistency
Caption Preference (Model vs. Human)
Category
Total Samples
Yes
No
Model Better Human Better Both Good
Both Bad
Icon
210
97.62%
2.38%
43.81%
18. 10%
36.19%
1.90%
Illustration
210
92.38%
7.62%
46.67%
15.24%
35.71%
2.38%
Animation
84
95.24%
4.76%
46.43%
10.71%
41.67%
1.19%
Overall
504
95.04%
4.96%
45.43%
15.67% %
36.90%
1.98%
Table 23: Human evaluation of synthetic SVG quality.
Category
Defect-Free Defective Human-Likeness Score Code Quality Score
Illustration
96.19%
3.81%
4.20
4.31
Animation
93.81
6.19%
4.07
4.23
Second, to verify whether the perceptual and semantic quality of SVGs generated by our model aligns with human preferences, we conducted a systematic human evaluation. For each participant, we randomly sampled 5 icons, 5 illustrations, 3 chemical structures, and 3 animations from the Text-to-SVG Text-1o-AANI and Image-to-SVG / Video-to-SANi evaluation results for scoring.
In each task group (see Figure and Figure 16d we presented the participants with the input text description or image, along with the SVG outputs generated by six models (GPT-4o, Gemini-2.5- Flash, Claude-Sonnet- Qwen2.5-VL-722. OmniSVG, InternSVG) in randomized order. Partici- pants rated each output on scale of 1-5 across multiple dimensions, including Semantic Match / Visual Similarity, Visual Quality, Human Design Alignment, and Usability.
(1) Semantic Match Visual Similarity: Whether the output accurately fulfills the input description or faithfully reconstructs the image content;
(2) Visual Quality: Whether the graphic structure clear, concise, and stable, and whether there obvious defects;
(3) Human Design Alignment: Whether the SVG conforms to common human design intuitions (simplicity, structural clarity, compositional balance, color harmony, etc.);
(4) Usability: Whether the generated SVG can be directly used in practical design and editing workflows.
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Text-to-SV6: Generation
Text-to- SVG: Illustration Generation
Canse Shoee
Text-to-VVG Chemistry Generation
Text-to-SANi: Animation Genaration
Comple
40%
Intenees..
Cervin Betaeh
(a) Human evaluation results for Text-to-SVG/ / SANLL
tVG: Icon Generation
Image-to-5VG; illustratioon Generation
Uscal S-n cante
Alperoce
Haran
Clett 400net
Clida Servec4
25
Ons/OR6 54
Image-to-SVe 5VG: Chemistry Generation
Video-to-SANI: Animation Generation
Manan
Obuda kmo04
BP743
Clede Scroes
denoeu 5.5
09746
(b) Human evaluation results for Inage-to-SVG/ / Video-
Figure 17: Human evaluation results for synthetic SVG generation tasks.
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The user study results, visualized in Figure 17a and Figure 17b demonstrate that InternSGG sig- nificantly outperforms other baseline models in both adherence to user input instructions and im- age reconstruction fidelity. The generated SVGs exhibit higher quality, clearer and more stable graphic structures, fewer defects, and better alignment with human design preferences and usabil- ity. Moreover, our quantitative evaluation metrics consistent with the user study results. For example, in Image-to-SVG for icons, the user evaluation results show InternSVG (4.64), Claude- Sonnet-4 (4.00), OmniSVG-3B (3.90), Gemini-2.5-Flast (3.73), GPT-4o (3.72), and Qwen2.5-VL- 72B (2.42), presenting a trend that is largely consistent with our quantitative metric results.
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C DETAILS OF SAGOGE AND SARENA
C.I STATISTICS OF SAGOGE
Table 24 presents the statistics of SAgoge in its four domains. The Icon subset contains 2.8M SVGs and I1M samples, with average sequence length of 846 tokens, which reflects relatively simple structures. The Illustration subset smaller in scale, with 600k SVGs and 1.6M samples, but has much longer sequences with an average of 8.7k tokens that indicate high structural complexity. The Animation subset consists 61K SVGs and 122K samples, and provides dynamic elements that capture temporal changes. The Chemistry subset provides 1.7M SVGs and 3.4M samples, and its average length of 1.7k tokens shows the rich geometric and semantic details of chemical diagrams.
Table 24: Data statistics of SAgoge. #SVGs denotes the number of vector graphic files, #Samples refers to the number of training samples, and Avg. Tokens indicates the average token length of the SVG code.
Dataset
#SVGs
#Samples
Avg. Tokens
Icon
2.8M
11M
846
Illustration
600K
1.6M
8673
Animation
61K
122K
847
Chemistry
1.7M
3.4M
1752
C.2
STATISTICS OF SARENA
Table 25 shows the data statistics of SArena. SArena encompasses multiple domains (Icon, Illus- tration, Animation, Chemistry) and diverse of tasks, including multiple-choice QA, editing, text-to-SVG, image-to-SVGr and video-to-SVG, Here, Avg. Tokens is the average token length of training sample.
Table 25: Data statisties of SArena.
Benchmark
Tasks
#Samples
Avg. Tokens
Mutiple-choice QA
6012
1197
Editing
2000
3175
Icon
Text-toSVVG
6013
1118
Image-to-SVC
6013
1169
Text-to-SVG
2001
8181
Illustration
Image-to-SVC
2001
8291
Text-to-SANI
504
1677
Animation
Video-to-SANi
504
1719
Texi-to-VVG
3003
1010
Chemistry
Image-to-SVG
3003
1065
C.3 DATA SYNTHESIS PIPELINE
C.3.1 ILLUSTRATION DATA SYNTHESIS PIPELINE
As high-quality open-source SVG resources for illustrations scarce, establish dedicated synthesis pipeline to expand our dataset. The overall process depicted in Figure 18
We begin by employing GPT-40 automatically generate diverse textual prompts covering wide range of objects, styles, and scene descriptions. These prompts are crafted to encourage the gen- eration of vector-like visual features, ensuring compatibility with subsequent vectorization. The
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<lett 1ty4, simale vester white red monnng
GPI-4o
Instructions
FLUX
Image
Figure 18:
Illustration data synthesis pipeline. Textual prompts generated by GPT-4o are pro- cessed by FLUX-based model and converted to SVGs via VTracer.
prompts are then processed by FLUX-based generative model equipped with a vector-style LoRA adapter] This step ensures that the rendered images are not only semantically consistent with the prompts but also stylistically aligned with the characteristics of SVG, such simplified shapes, flat colors, and clean edges. Finally, we apply VTracer to convert the generated images into SVG for- mar, which performs vectorization by detecting paths, curves, and fills. This automated workflow bridges the gap between raster generation and vector representation, enabling us to systematically produce large-scale, diverse, and high-quality dataset of SVG illustrations.
C.3.2 ANIMATION DATA SYNTHESIS PIPELINE
High-quality open-source SVG animations extremely scarce, making it infeasible rely solely on existing resources to achieve the desired scale and diversity. To overcome this limitation, we exploit the powerful code-generation capability of Claude-Sonnet-4 synthesize SVG animations compliant with the SMIL standard. We design generation protocols with explicit constraints, such fixed canvas size and mandatory animation primitives, which ensure structural consistency, ren- derability, and suitability for large-scale training. This pipeline enables systematic expansion of the animation dataset and produces SVG animations with richer semantic content and greater geometric diversity.
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C.3.3 EXAMPLES OF SAGOGE
<seg xmits= www.///ww//w/// 2rg/2000/vv""
adth-"118" height vedess11 0
96-92.6765 10.66-42
width="1 29" height= 125" viewBox-
5.33 32:0 254-24 5.34-5.33 5.34H1 68-2.88
5.3658.67c0-22 2 294-244334--355 3,36-5556 5,36m--
123 1p8t> -path 111- "0ua0e0"
10.66 10577166 587 0 10.07 48-10.5 0.57
d-12117.33 53.334425700 3.8 4.8 10.57
0867V7467c2.54 5.34-24 5.36-
54x00367722
95 3148V4257H1
B 200000//s"
0770-2 54 2445.34
</Sxg
96 15
the shape object? A) Cirdl
Quection: What
Bi Rectangte
Traangie
Square
fll "14448 d-'m99 85 97.25-233333 233c-
6455355 63.50 1270 63.50
56 95 75 6.7 01 05.55.675
(660'>-cpeth 11
(f/a-pelh i8--22406"" d-1M22
1.35 1 91-15 15-475-15114277 78.55--
50200 spae-pponiii fil. e171119 d-1M32.5
$2.61188 198
$2.5255 25.1 01 010502 25 155122 25.1 011
1:15- 1111-12515---44222
1520085
128
"1-"" d-1M4113
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19.119
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d="m6617
glass?
Question: What handle the magnitying
Yellow
Blue
Green
Category: Geametry
13.5 0M47.3 41 12235 010000550255
2:95 0 0-5.900--s//a-
Category: Colar
opq wit right lat dest "577aa"
"5ig smins- wttps///wwwwtttooommm 2rp//200//vg
deer weedte poeat it anl
witht-"1 28' height- 128" ceeeaoo- 0 128
2 550465 (441)) 4ed 4 251 4204
2 24448 44.c- 24 24432 36
0 7.2 5.5 13.6 13, 13551368
7,2L55 2 edl
Br2 414
17.5-72
416
21.22.
Question: How many hands does the clock have?
2014 18 d1 4Bc 2020
167 2.11
12:9-
whole! dragen symmbol
Question: What
object
23
595576
41.50
6
25.6 57. S2cD
4
31.6
312
75 58 57.
25.4 56.4
14.8C16
Three
One
E 1 214 SINTE Bed caile 4he
Ei ald M44 to soctc and a Tea7 22.00.220
anstammnno Englage Dy abstract art
Pythan
57.6 56 11110-490
/>>////>>
D) Four
Te 2 46 1005 4/44
Category: Quantity
100SOM52 167 37 25 321 7170
24:201 </oig>
Category: Semantic
2000/W0"
withh-112" "128" haght- 122 fil- "hore"
coup ambres Nette ong hon 12t
weedica
o-ath
de 23 add de 6,00 02
dtoke witth="2 d- "Mee 100 100 spatt
[1=1133365F de 1999 35 38.475533 5.30
22W uue 3 6.10 0o0
opath cocke #1316"" et Srecape "ound"" stote 22 d-1M56
2002
10600DAT057 10 0
5067516M4233
11.30455 3aal0 S7 1047 0
B4rr8 2666510
the object whole?
Queetion: What
Do 1M6ku Bess on 144 1.21
The
B-7 Maa
the star?
the color
11.35655 57 0.00000000000000000 10.66
A) lamp
1744 4. 41 Mh 119-1 sdd-
A) Red
<path excke #11163" etrak--addts-
106000A1607 67 0 B1 347/
CLA trashy
B) vase
28 & 21
0358 44551344 09
187255
Bl Gresn
E0.59V40J 936
10 677474506741367 10.57 0 40
1-08--07-1
#bwili/aaa7..3 33 bo D. mu
Category: Semantic
2.43 1.4 43522350 16386
14 125
Category: Colar
DC 1.2-3.75 cip rule- "owenodd <ligg
21.33 0 288866 60
witth 288 height= 128" stewBorr 0 128 >28">-0
<ing sminse 3ttto///wwwrd ony 20002000//v
V0 24"xxg stroke- <path D
with- 128' helght- 128" fll- "none"
<ova umits- "wtp://wwwwaaaaooo 2r0/2000/sv""
di-"1104.06 06 T1 2---47-061-79 78-27.16-
5:25111 74 10.1 .4.02 (2.1 13,05c1 49 1.66
12 12 19c-1 17 18-1 09 1.7%
1,45 4:1111111 711-54001
"aoondddd=ppttthla 80770000
32
Question: What
the primary shape
1.5c. 11 11--22272733222 27-3172333333333333
A8.01 59 07 1.1244
1.45-911--00 901---11111222 23-
1.06 09 22::22222222222211 1.6.3166666
1,021
4.85 8:
0.37
distinct outlines are
Quertion: How many
the orange part?
100020017
present
5.784.01 32.34L48.8 2.65 43.85c-3.3 3.53
56 9.43 5959 29.79 17.361225-111
1.15 0 C- 24142 72 p4 B5104- 00
12.33-1 2:55c 147-1 65 1.3-4-12111 1211222
211"/> <igo </tgg>
12.-12222411 2a4.15
Category: Geometry
BlAroo Traangle by Square
3.44 24.38 28 56.759444445.6 34.44774.46 1,74.65
3.Mu5.011 2 1-46
234466H8.14 14r-2 0-1 O.
33555/
14.952
24- 3-55- 94-9403
11-1127
qy-118"
Category: Quantity
C) Three
One
Twu
Four
Figure 19: Examples of multiple-choice QA tasks. In this task, we only provide the SVG code, requiring the model to answer the question directly based on the semantic and structural information contained in the SVG code.
39

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Published as a conference paper at ICLR 2026
Question
<tug amine http::/wwwwwwddcccccccco 200l/ng" widh
125 hegg
120 vewiooe
00
128
de M69.76 14.354424 43.5
11 DA45
45000
10
1400
25
07
5468A1817 17:96
0
0
17.3 00
280V66le66 00
6RM50 R2
000
12:32412.01
03
spath
25.0/347
16.2
B2VB226
0.0
2.15
1-1.54-2.1111111
11.442.11V1044623
666-6660006 6-000-
110V5455
16.07 1 80 70x2
000.440 0-12 12 24 0 O D450
47.47..6 D 0.0.-14.77
5,120.2 395c-
1,47 07-22 04-67-44 0uME2 54a15 07 15.97 O.D D-12 4.44441 C1 44 DO 69 57.5430 5430.55 30 0.00-3367
21 -4 5.2122 11.60 3:22 17 26.56 5.57T11 13,7 O OO 54m34 55a O 1-3.4 7.51 9.88 9.58 DO 2.AT0.2
10 0 46 109 18958Y100422 20 01 2-2
1.55 0 11 143.11A 52 1.52 D
104/5 76 6.66 0 01660004.99 0.99
6.1AA6 6 0 62 (11994h/cmb--200 2282 2 1-2 2H5822 20 D-4h1 232 O 222m--0h--20 6-----V544---- (1554441446666666 14.60
2: 32.2222222 20 0-17.33 10.05c- 06 16-12-3 3-15
2.11 2.2-552-1111-2
13.8-4.51 19.3
11.5 8.87-19.5 22.02
1315
96 12.55 22.00 2415
49-2. 8---4551
12.51- 4747722 20 0 50m
4315.87 0 0-9:4 2.98:2.00 02-00-5-55 3.96 12.06 3.221 12-2272222 26330.6666
30.26 0 0 10-0-337-211111 21722229964439777 43.97
</oo>>
somantic geometric
Response
The umbrella in the code stylized icon with several distinct geometiic and semantic features:
1. "Shape and Structure**
into circular pointed top, resemblinn traditional umbrollaa. the The handle is straight line with hook at the boffom, typical of umbrella designs.
canopy
2. "Calorr"
3. canopy light
accents, providing contrast depth.
The umbrrelia symmetrical along its vertical axis.
The lines dividing the canopy are evenly spaced, enthancing the symmetry.
4. "Sylization""
design simplified iconographic, focusing on
convey concept umbrella,
5. "Qutline"",
The entire umbrella, including the handle, outlined in darker blue, which helps define its shage clearly. features combine create clear recognizable representation an umbbeella minimilist style.
Question
Code:
xmine- 'http www.w//wwwooo... 200/20000sgg" width-
"120
128" -poth "ilo777
d24 23 S4V 54V124444000 43H44844482000o de 9.66 0 3.6 06.07.566 7.56-8116
1-
15:7556811
15 8.15754-8.6 15 151281669.03
3.16 7.56 8 15,15,11 15,12 16 7.54 8 1- 7.66 8:16 15.12 6:1128167..55
3.62// <pach fill "ette44 d-1M96 6-25H32VEn6a5 0 Ohda6 0 5 500 0 CaM104 50
7.56-8.16
0
1 1.50-58--445 46-12 12:4- 45-8 (1-22 18-17.6 Sy9a610 0 0-20 0v11 26c-44 68 5-178 5-6 0-646-7..8 84-
124-446V58h6u88 8 0 0h12v1 2V5hh 1238 0.000 C2 -ath
"cc77000
d- MED BhBveh BaMGo
18hsudh-aJMB2 htter//wwoo5sssss44aaeaaa
o 523225224448 8 0 1-8-8M40
18hrw8r RxMB4
20022114484 760t 2V1G5222444 9tv16. 2cc 26 54.02 82.02 0-9.0-5.550 2.522-5..222 18.30-5 30-3300---20000 0-54
9.0299Eu10 0
</arg>
object
Provide
breakdown
semantic purpose notable geometiic
9. angles, curves, proportions).
Response
The code depicts stylized sandcastle. Hore are some geometric and somantic features:
"Geometric Features.**
"Rectangular and Square Shapes The sandcastle has rectangular and square elements, forming the base and towers.
"Curved Lines** The top of the castle features curved lines, representing battlements.
"Symmetry" The design symmetrical, with matching towers on either side.
"Semantic
"Sandcastle Regresentation.** code symbolizes sandcastle, commonly associated with activities and play. "Color.* The yellow and orange hues suggest sand and sunlight, enhancing the beach thame.
"Playfulness"" The overall design conveys sense of fun and creativity typical of building sandcastles.
Figure 20: Examples of SVG description tasks.
40

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Published as a conference paper at ICLR 2026
Question
hhtp:///wwww
width-112P 120 height-
d- 050G2 42c-3. 3.57 3.57-22 3.79-4
22.07
9.90-38
70.11
22.09 22.05530 18.53
999/hb645c1
11.09
5-091
255
5055-64
76m66.52
2 31c 36 0-7. 16- 13-.45..
24-22
34 06-664222
28- 3.78
4,055
68.67.76
75
06
11.52-1.117
10.-7.25
10:25-22 22. 35 8.25-1% 74- 06- 16
16.04-15 73-7-1
44.4.
09:41
1.2
8.23 6:09
1305 11.14
4,55 11:35:2.51
57.62
1:21 (11-88888991
252
1.232.00 0
1.22
1,452-2.33
11.32 2.25 26.49 6.25
33.74.38 26.78.1155555 52 1.17-3.22 25, 3.0544--22222
52. 75.05.77 73:21 06.36
1--24.7- -5.43-
-othh
#li="=19tSc1 d-
MM4.57 103. 154433
-8 0-1
0-1223-55 23-55-122
22--1225-552
2.225-5-11
245-124661..
14:58
1,23.66 1.23
1,24--55
.23M933.0
D..OHHEAAD D4c
1,23"'> opath
15.66 31.53 47 44..5653344 55-44577-44
14.85/62.44c-22 76 1.38-.8-
1.87-44 B81/) Sil=="e7e7eel Rf 3M.4. 100 688-58 44c- 18-2. 32-4 50H55,, 12 2:22-22 441-44
6.59//> </ogg>>
(chapes, symmeerry, layout) and the conceptual meering (schal representtss symbolic features, color implicationi the resulting image.
Desoribbe both the visual
Response
The code depicts sleeveless shirt with the following properties:
Shhppe" The shirt has simple, elongated shape with rounded neckline and armholes.
"Symimetty"" it vertically syminetrical, with both sides mirroing each other. "Uiess* There are two honizonial lines across the lower part of the shirt, creeting a banded appesrence..
"S "Semantic Properties
"Calor": The ssirt light blue with a darker blue outline.
"Style"*: It resemblee a tank top or sleeveless athietic shirt, sugpesting casual or sportswear.
"Désign": horizontal lines may indicate design feature
Overall, the shirt has straightforward and functional design, typical of casual or athletic wear.
Question
hhtp:://wwwwr
ong/2000/5wg width-"120' height- 122"" viework-
dM 038006624000 48c-3
333-3.777 3.79-65
11117-37-100517.---6 10.51007551110000 10.70 0207702------
94-14
40C77
70.11
2 31c 36 0 16-93 43-43-333003 24
22.09 64 22,06550 94 18.33 48.75 7756..777 9.37h (11-64551..8
34-64.22
85 11.59
3.78-23
23.16 4,05
3:5 24.54
48.67.76
75
2.02 50.53
06 46.14
S1
21.52-11.11 10.5-725
73.76H66.02
$
10/25-22 72 35 8.25-1% 74- 06-
04- 73
21-1-1100
011--8---000 08- 64.94
(9:41
1
08
8:23
6:09
13.05 11.14
4.55 21.35-2.51
13.0-11.1412---
57.62-
1:21 -8188.333
252
1.232.0
1,22
1.452-2.33
11.3P 2.25 25.49 8.25
33.74.38 26,55 58.75811555
52 1.17-3.2
25. 3.05----2252
$2.05
F2.05.76
75.21
06.36-0677-2222
1-24.27-
01.27-58840
1.23.56
1,24.-55 1,23-1.23
223M333.0
53.
23:59
1.24555
1,23-1:24
spath
31.53 44.356M33.42
14.85%62.4B-72 76 1.36-9.8.
1,87-4. 887)>-pwwhh 6il="#e7e7edf wet 3M34 34.77 100 688-58.444 44c- 18-2 37-4 47--66-6 59H155 12 2 2.22--2 441-44
6.59%/² </oog>
Describe both the visual geometry ishopes, symmetry, layouti and the conceptual meaning (whot repressnts, symbolic festures, color impliattonsi of the resulting image.
Response
code depicts
shirt following properties:
"Geometric *
""Shap"": The shirt has simple, elongated shape with a rounded nedidine and armholes.
"Symmetry"" It vertically symmetrical, with both sides mirroring each other. horizntal lines the part of the shirt, creating banded appearance.
"Lines"": There
"Semantic *roperties:**
"Coloo"", The shirt light blue with a darker blue outline.
"Style"": It resembles a tank top sleeveless athietic shirt, suggesting casual or sportswear.
"Design"*: The horizontal lines may indicate design feature
Overall, shirt has straightforward and functional design, typical of casuel athletic weat.
Figure 21: Examples of SVG description tasks.
41

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Published as a conference paper at ICLR 2026
Instruction
You are specialieed SVG editing assistant.
Please apply color transform to the grashic
Change vicbda? to mediumblue (F000CC)
Low-level Color
Editing
Instruction
You automated SVG modification
utilyy: Crop the graphic show Crop top half of the image
Cropping
Instruction
You are specialized SVG editing assistanc.
Flip this SVG graphic Flip horizontally
around center vertical axis
Flipping
Instruction
editing graphic around
Rotate cunterciociwise 24.50 cogrees
Rotation
graphic.
0.65x.
Scaling
Figure 22: Examples of SVG editing tasks.
42

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Published as a conference paper at ICLR 2026
Instruction
You are on automated SVG moditiction
utlity, Add stroke oudine the SVG shape.
Add mecium purple outine with widhh
Adding Stroke
Instruction
movement the SVG. 17.84 down
Translation
Instruction
Act as expert SVG code manipulator Set the SVG graphic to transparent effect. Set to very transparent (opacity 0.12). A
Transparency
Instruction
precision SVG ed editing Change the SVGs graphic different color the handle color darkgray.
Semantic- Color Editing
Instruction
Replace left with woman with bob haircut keep colors, byyout unchanged.
Style Transfer
Figure 23: Examples of SVG editing tasks.
43

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Published as a conference paper at ICLR 2026
Instruction
generate expert SVG generation, you will be instruction. Please
Instruction: computer monitor displaying pie chart and bar graph on blue background.
Response
Instruction
You are master of SVG generation. Please generate an SVG file that fulfills the following instruction requirements. Instruction: parked inside garage with the door open.
Response
Instruction
specialist creating
requirements specified graphics. design
Instruction: The illustration shows one clown front facing pose, holding red balloons, The down wears blue outfit with striped socks, red shoos, and rounded shages, blus, yellow, whito, medium weight solid strakes. The composition symmetnccal, with the down centered and the balloons either side.
Response
Instruction
You master SVG generation. Please
SVG file that fulfills the
tollowing instruction reguirements.
Instruction: The illustration shows one squirel in sitting pose, hoiding an underbally, and fills
acom both vector features light
bodl.
rounded shapes, smooth brown, white, and beige. with medium weight solid strakes. The compasition
contors the squirel, with the acom close its chest, creating balanced
simple layout.
Response
Instruction
You are professional vector-based image generation. Please produce SVG that matches the following instruction.
(45)
Pyrano|3.2 cjpyridine-3 carbonitrile
Response
Instruction
master generation. generate
following instruction requiremennts.
Instruction: Structural formules of hta.rr---nnntt-------eee///ppppp 1-y0methyl)--a 2it-22tttoo-----o---
Response
Figure 24: Examples of Text-to-SVG tasks. Line 1-2 show icon generation, Line 3-4 show illus- tration generation, and Line 5-6 show chemical structural formula generation.
44

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Published as a conference paper at ICLR 2026
Instruction
expert generation. After receiving
image and an instruction, output an SVG that satisfies the instruction.
image: <image>
Instruction: blue dircle the number seven.
displaying
7
7
Response
Image
Instruction
Please generate SVG file that fulfills the following image and instruction requirements.
Image: <image>
sloth side lying on branch with closed eyes. The sloth has light brown face, dark brown hody, and small rounded ears, The smooth brown, green, beige. rounded edges, and flat fills in salid composition centers the sloth on the branch, with leaves on either side, creating belanced layout.
Instruction: illustration
Response
Image
Instruction
You SVG generation. Please generate SVG
following image
requirements.
Image: <image>
Instruction: Structural formulas
Dibromonaphihsllne
Br
Response
Br
Image
Figure 25: Examples of Image-to-SVG tasks. Line 1 shows icon generation, Line 2 shows illus- tration generation, and Line 3 shows chemical structural formula generation.
45

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Published as a conference paper at ICLR 2026
Instruction
You expert in SVG animation creation. Given instruction, generate animation that follows Instruction: black icon animates on white background. The icon starts as horizontal line with vertical line extending down from center, ending a rounded tip. The raunded tip then transforms, spreading out to form V-shaped arrowhead. The final form downward pointing arrow horizontal line above
Response
<svg xmins= http:://wwwwlllrgg2200////// width="128' height="122" viewBox= 0 128 128"> > fill="none"
stroke= currentColor stroke-inecap=-roull "roup===rrdd""" stroke-linejoin= "rund"
<path stroke-desharra="
stroke-dashoffsen "106.666-11 d-
"freeee attributeName- "stroke-dashfffet dur- "0.2s" stroke stroke-dahharay= "85.33" troke-dasholfset= "85,33" d= 37.33v72'> <animate "Blues="85,3330 "85,33,0" <>at/pth>>>>>>>> <path attributeName== SaauuttNamm=sstroeeeeaea "stroko-ddcchhffftt begin= du2="0.2s" values
dasharray- "64" "64" stroke daakke-dssffffttt- '64" d="m64 112 37,33-37.8 333-37.33M64 112 26.67 74.67* 6 <animate fill= freeze"
attributeName= stroke dashaffset" begin= "0.5s" dur= values 164,0 </path> </s/g?
Instruction
You are a skilled SVG animation designer. Based on the instruction below, produce an SVG animation that meets the requirement.
Instruction: stylized signal icon, composed of circle white background. The circle at the icon's base repeatedly changes color. It flashes from dark, solid blue to lighter, muted blue and then back again. The two arcs above it, one medium blue and the other very light thraughout the sequence.
concentric in shades blue, centered
blue, stay
Response
<svg xmins= "http://wwwwwl. httpw/wwww////..//////o// org/2000//// width='128 112" "128 "las=rids-radio" preserveAspectRatio== "MMilYMMMa cy= r='14.08"
style= "background:
0 128
canimate
attributeNamee" fill-opacity" begin= 0s" calcMode=
dur= 7" ",0Tmme==0000.211 repeatCount= "indefinit"
values= '0/1;1'/>> </circle> <path fill= none' stroke= #5699d22 wirthe 110 d= "M35,04 60.16A35.84 35.84 0 71.68 96'> canimate attributeName=
datteeeaaaeesss-keeeerro "stroke-opaciity begin= "..89999999999999 caleModes 'lineur' dur=".7 keyTimes= '0:0.2:1"
repeatCount= "ineffinite" values= '0,1;1'/>> </path> <path 'nnee" stroke="#dSebft "#d8ebf9" stroke woikk--withh-1000 de "M35.M8 35,884 32a64 64 attributeName== sstrcke--tttotiiitty begin= 991899999999999
key Times '0,0,2:1" repeatCount- 'indffinite" values- 0:1;1 1//11/>></// </path> </svg>
Figure 26: Examples of Text-to-SANI tasks.
46

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Published as a conference paper at ICLR 2026
Instruction
an SVG animation expert your task transform the video and instruction into corresponding SVG animation.
Video: <video>
Instruction:
shown against a background. icon depicts bright yellow sun pulsing cloud.
large, fluffy white cloud. Behind raindrops fall bottom white cloud, disappearing as they move down screen. clouds and sun
rays, partially
grey cloud Light blue
slightt gentle bobbing motion.
Video
Response
uminss "http://wwwwwww33 ong 20000////// width="128 height= viewBox- '00 128 128'>-cdels> IineerGradient id= 'd" "2.884
x2="58.15 y1= y2=
offset= 0 colors #f3fffe estop offset= '5'
"#r3fffee// estop offset='1 stop-colors" *ddeeabb/ / -innennrrrennnnninnnn
11:1888 33.35" y1="24" y2=137.33"
gradientunitss "ssrrppacOUusee estop offset- stop color- "#9casaf"> estop offset- stop color- "eScc""""""""""" /s estop
stop
'1=61.62 x2= 4,52" y1s y2="5.72 gsadientunite" "serrpacconuusee <stop offset- 10" stop color- trac3dh estop offset stop color- "866c3db /s <stop affeat=") stop-coloo coloroooos-ss- "fsaale"// </linarrradiant
color= 1e66 1280 ys </linerGradieen- dinearGnadentte
diieearrrdeeen id='t "2.5 x2=1745" y1=" 2'1' y2=2222 href="4a"/== "aa//"aaieedearraaaae Gi-dineerGradientt Gdddientid==) id=") x1="118.5" x2= 13055 y1=22:1" 22.92..
href="4a/> 4a/>> dineer Gradient ide "6" 11.5" 22.5" 158855 y2n 33.17" gradienUUnits= "user SpaceOnUse"" > offset==0
color= "err="2bb""//
offset=" 15 hoopcolor=-eeiee w=
offset= stop-color= "err:="esebbb>>> //ne///nnnrraaiiennt -symbol id="""
viewBox= 196"
"245" oy= 24.5
"ur(+b) strokes "888f11"" stroke-miterimiit '10 stroke-width=" 11":
strake
linecap- "round stroke miterlimit- stroke width-"2"
'M245 5m0 6.35m11.77-22
4.47M9 25 45.4.....-444464.... 48m0 23.5 23.52-4.5-45mm 4..44.453.33.22 32.52 32.52-45544 122-4.5-.5511. 24..M......66...... 55m35665.m3665 65 Oh 6h-6.35"> canimate Transform additive= 'sum"
attribureName- "transfom'' dur- "6s" regeatCount- "indelinie type-"rotate" "rotate' values- 24.5 24.5:45 24. 24.5/s </path> </symbol> <symbol
id= 'e' viewBor="O 0 200.3 126. s-path fill= "rl(4e) stroke= 98486966
stroke-miterimit= 110 d= 23.3a6. &1 000&1
411122.66- 40222466-21..777 02.6.7 8.7 8.0000000000 1.63-17.238 13- 253-625112211 15 12.15 0.008066666 15 37e8.1 8.788.8.1111 8.00088800::000 8,07v055/=<///bbdd
esymbol id= viewBexe 350 222"
fill= "urjedd" stroke #ebefff stroke-miterimae
stroke-width"113 d='m72.75
26.75-63 53.022332
10.75a14
2.25
297511-05w05555e0
viewBox- canimate Tranffrm additive= "sum" atrributNNamee "transfom" durn repeatCount= "indefinite" types "ransatannsatte v 2.25
s/symbolls csymbool
cuse
200.3 height-
href- "te" transform- trandlatte(99.5
repeatCounte "indefinite" types "trnnlatee" values=" -455 0: 4.5 D -55 0'/2 ///////s////////// h/////booll-symmoooo.. csymbol id= "trnssoom""" dur- 16""
2.25 0'/s </useo cuse width- "350" height- 222" hrof- "6f's canimate Transform additive- "sum" attibuueNaame
100 >58*>-cuse cuse
width=119e h996 hree="Ag"/>>> #r6c3= "86c3db'
"39th "398" height= 222 brefs transforme "translate3321 21 9///<//ymbbll>> -symbol id='I
viewBox= 0 25
fil: "uf("a]"" stroke=
stroke-mibrlimite
d= d=3M3.12.125
3 0.0---33 opacity= 00*>
canimate Transform additive= attributeNamme "transform' begin=
key
type= "translate"
values-"0 11.5; 021.5 4.5 18.5'> canimate artributeNamee 'opaory" begin- and A2s" 585" keylimes- '0 71; values
1/07////path>> sath "ur( stroke= 3686 stroke-mitelliitt
1M.....221222 12a3
330000000000 0-3--3 opacity"""" 00">
canimate Transform additive= "sum" attributeName= 'transform' begins 675, ened---- dur=" 58s Teresiieeeeeeeeeeeeeeeeee Time== 02 .71 1" type= "translate"
volues= -1155 0 21.5: 0 335//manimtte attributeName= 'opacity" begine 67% y2 42s" dure 58s keyTimes=" 0. 14, 71; voluess 10
or/>></peth>>-e happphh--ppptttt spath fill= "rr strokes 448:3dd'' stoke-mitellimtt '10 d=1M31 12.12a3 3.0.
333 0 0-3-3" opecilys 10*
fransform additive
sttributeName= "trassfoom begins 133s and 42s dur=".500 leylimes=
type="transltee
"0 11.5,
18.57s canimate attributeName- "oparity" begin-
dur- '.8c" koyTimes- 14,71; volues- 137"
1: 1:000> </patho </aymbol> </defs>
'10.8" height- 2258'
transform- "translate(14 27.25
height= 25" "reff"ef" transforme" "raansslate///6 38// 4/sug>
Figure 27: Examples of Video-to-SANI tasks.
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Instruction
You are an expert in animated vector graphics. Create an SVG animation that reflects both the video and the given instruction.
Video: <video>
Instruction: A graphic animation on white background shows a circular logo composed six curved, crescent- like segments. The segments are arranged in pinwheel camera aperture style. The colors the segments change repeating cycle. The pattern alternates between color scheme dusty rose, chartreuse green, and light turquolse, and scheme where the turquoise segments become pale yellow.
Video
Response
<svg xmwns= whtppp//wwwww3.rr////00/00 width= height= '128" class 'Ids-blank'' preseveeppeccReiio--MMMMMM
view8ox='0 0 128 128"> > ag-aatr-transform "scale[[configgscall scalel)) transform "rotate(33 35.994 79,206)scale(8)7
repeatCounts
type=
64,0 64 <p"thhppthhheeeee d= 'M5. 14.22 29.6-55.1 297-96 41,05 4,0316. 19 4--..21-1....-1....... 16.1--2..1112...11--31.. 5.47-9.79
5.62-17.14 24442222222442222222222222222233 254
17-37.59* genimattettrbbbbeeeeaa-----
begin= 11.5s' aallModee dur- 200
repeatCount- "indefinite"
18.59-10.94-29.66 0-11.38 189-22.0 21111111113222444444444444222221 <path mm4411 78.4 2.74:5.4--611---705----
4.9-24.05 18.29-24.06 33.56 0 14.84 9.65 28.38
24.06 <3.m7>=< attributeNVam===1ili Cacea dur=
16.71-3.03 httpa//wwwoo.o...oooooaooaaaaaad
repeatCount= "indefinite"
58c8.33
8.21c-
3.89 15..1117777..23555544444000003.0
calcooee=
dur="2" key7
s/paths-<pathe 1.59 6.19 588c662 21t.://4.......22222222222222110 o22.
5.47--..1-
atributeNamm==""ll bo: httpa:/eeoeoocoocoooooeoooenonen
values= htt.::/iiieleelieaeeeeeeeeeeaeee
6.197.05 10.94
d= 38.8-111311111111111
7.6429967--99-441554000---
-1----188-------999---3----- fil calMooe
dur=
keyTimes= 20,.=,,000002,,,50..7:1111 meep "epeaaCouutsssnddiiiie hrtpr//cceecoooooooooaooooaaaanr
</svg>
Figure 28: Examples of Video-to-SANI tasks.
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C.4 TEXT PROMPT TEMPLATE
C.4.1 TEMPLATE FOR DATASET CONSTRUCTION
We randomly sample from the following prompts to generate the SVG description data.
"pescribe in detail the semantic geometric features of the object shown in the image.",
"Detail the semantic or geometric features of the object in the image. ", "Offer detailed description of the geometric or semantic features of the object in the image.",
"Give comprchensive description of the semantic or geometric properties object depicted in the image.",
"Provide an in-depth description of the somantic or geonetric aspects of the object shown in the image.", "Explain in detail the semantic er geometric characteristics of the object displayed in the image.",
"Could you detail the geometric or semantic features of the object in the image?",
"I neea detailed description of the geometric semantic attributes of the object in the image.",
"please describe the semantic or gecnetric features the object in the image comprehensively.
"Provice a thorough description of the geometric or semantic properties of the object in this image.", the semantic or geometric features of the object in the image?",
"Can you elaborate
"What are the geometris or semantic features of the object in the image ?",
"Describe precisely the semantic geometric characteristics of the object shown the inage.",
"Provide a detailed analysis the geometric or semantic features of the object in this image.", "Elaborate the semantic and gecmetric characteristics of the object shown in the image."
For another form of SVG understanding, we design multiple-choice QA tasks, which are constructed using the following prompts.
Given image rendered from svG, write exactly four multiple-choice Q&A items about the depicted object.
Q1-Q3: ask about visible scmantic or geometric propertics (e.g., color/ http://ssttoooos//////eeeeteeeee
04: ask about the overall identity/meeaning of the whole object. Each item has options A-D, one unambiguous correct answer, and three plausible distractors.
Use only information visible in the image; avoid "All/None of the above". Output (JSON array):
("q": "...?", "choices"; ("A": " "...", "B" "C": "D" "... "I, "answer"; "B";,
("q": "...?", "choices":
("A": " "B"; "...", "c"; "...", "D": ....",, "answer": "D";,
("q": "...?", "choices": ("A":
"B": "C"; "D":
...."I "I, "answer": "A";, ("q": "What is the object as a whele?", "choicos"; ("A"; " "..." "3":
"...", "c"; "...", "D"; "..."), "answor"; "C")
For the editing tasks, we design specific prompts for each of the 10 subtasks to construct the dataset.
The following code illustrates the prompts used for color editing and style transfer tasks.
color editing:
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You are given images: original image and edited version where certain colors have been changed.
You are also given the exact coler transformation information in the format:
'Change color [original_hex] to [new_hex]',
Your task is to generate TWO textual editing instructions:
1. A **simple instructione (direct color change, using hex code).
2. +*complex instruction** (semantic editing, describing what part of the object changed, e.g. "the 1eft arrow color").
#+# Example:
Input: Change color #00abff to 4D88FD8
Output:
Change #00abfg to thistle (#D8BFD8)
Change the left arrow color to thistle
Now gencrate the two lines of instructions for the following input: (descripticn]
Important: You must output exactly two lines explanations, formatting, no extra words. Only the two instructions.
Style transier
The first image shows Icapticn beforel and the second shows a different (caption_afters Describe how the first image should be edited to look like the second image. Do net just say \CChange to match the second image/emoji, but specify the expected result. Also, make the instructions For example, if a plane is landing towards the runway in the first inage and taking off in the second, you could say \"Make the plane take off\".
clear and short possible.
For other simple editing tasks, we use the following prompts.
# adding strcke
"Add an outline to this SVG shape.". "Apply stroke effects the sVG graphic.", "Add a border
the shape.",
"please add an outline stroke to this graphic.", "Add border lines to the svG shape.", "Give the graphic border outline.", "Please add stroke to the SVG element.", "Add an outer outline to this shape.", "Add boundary lines to the graphic.", "Please add border effects to the svG graphic.", "Add outline lines to this graphic.", "Add stroke outline to the SVG shape.", "Please add outer border lines te the graphic.", "Add border stroke to this svG.", "Add outline border to the shape."
translation
"Translate this SVG graphic.", "Move the SVG graphic to a new pesition.", "Flease translate this graphic.", "Move the graphic "please move the svG shape.", "Adjust the position of the graphic.", "Please translate this shape.", "Move the position of the SVG element.",
the specified direction.",
"Please adjust the graphic's positicn,", "Move the SVG graphic in a certain direction.",
"Please apply translation transform to the graphic.",
"Move this SVG shape.",
"please translate the graphic new position.",
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"Apply position movement to the sve.", "please translate this SVG element."
scaling
"Scale this SVG graphis.", "Adjust the size of the svG graphic.", "please scale this graphic.", "Enlarge or shrink the graphic.", "Please scale the SVG shape.", "Adjust the size of the graphic.", "please scale this shape.", "Apply size adjustment to the svc element.", "please adjust the graphic size.", "Scale the SVG graphic proporticnally. "Please apply scaling transform to the graphic.", "Adjust the size of this sVG shape.", "Please scale the graphic proporticnally."" "Apply sizc adjustment to the svc.", "Flease scale this svG element."
rotation
"Rotate this svG graphic.", "Rotate the SVG graphic around its center.", "Please rotate this graphic.", "Rotate the graphic by specified angle.", "please retate the SVG shape.",
"Rotate the graphic around the center point.", "please rotate this shape.", "Apply angle adjustment to the sve element.", "Please rotate the graphic.", "Rotate the SVG graphic cloctwiseeccuuteerooowsssss "Plcase apply rotation transform to the graphic.", "Rotate this sVG shape around its center.", "Please rotate the graphic by certain angle.", "Apply rotation operation to the svG.", "please retate this SVG element."
flipping
"Flip this SVG graphic.", "Flip the SVG graphic.", "Please flip this graphic.", "Flip the graphic vertically herizontally.", "please flip the SVG shape.", "Flip the direction of the graphic.", "Please flip this shape.", "Apply mirror flip to the sVG element.", "Please flip the graphic.", the svC graphic.",
"Apply mirror processing "Please apply flip transform to the graphic.", "Flip this SVG shape."
"please apply mirror flip to the graphic.", "Apply flip operation the svG.", "please flip this svG element."
# transparency
"Adjust the opacity of this SVG graphic.", sve graphic.",
"Set the transparency
"Flease adjust the transparency of the graphic.",
"Sct the graphic to somi-transparent. "Please medi the opacity of the SVG shape.", "Adjust the opacity of the graphic.", this shape. "Apply opacity adjustment to the sVC element.", "Please adjust the graphic's opacity.", "Set the SVG graphic to transparent effect.",
"please set the transparency
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"Please apply opacity transform the graphic.", "Adjust the opacity of this sVG shape. "Please modify the graphic's transparency, , "Apply opacity operation to the sVG.", "Please adjust the opacity of this SVG element.
cropping
"Crop this svc graphic.", "Crop the sVG graphic to shew part of it.", "Please crop this graphic.", "Crop the graphic "please crop the svG shape.", "Crop the graphic to half size.", "Please crop this shape.", "Apply cropping to the SVG element.", "Please crop the graphic.", "Crop the sVG graphic to show only part.", "Plcase apply crop transform to the graphic.", "Crop this SVG shape.", "Please crop the graphic specified area.", "Apply crop operation to the SVG.", "please crop this SVG element."
speci fic region.",
For the Icon generation task, employ the following prompt guide GPT-4o InternVL33-788 in producing the required instructions.
You are an expert in image analysis and description. You will be given an image of a graphic object.
Your task is analyze the image and generate exconcise and precise caption** that deseribes the semantic meaning and structural information shown in the image.
Focus on:
Semantic meaning and object identification
Shape and geometric structure
Color information
Key visual features (e.g., symmetry, number of elements, patterns) Spatial relationships petween elements
Avoid:
Symbolism, history, or cultural centext
Subjective interpretations Lengthy explanations unnecessary details
Generate a clear, descriptive caption that captures the essential visual and semantic information of the image.
Output format:
concise caption such as: A red five-pointed star with black outline
A blue symmetrical hexagon with white interior
Three overlapping circles in green, blue, and
For the illustration generation task based on Flux-synthesized images, which often contain richer elements and backgrounds, require MILMM generate detailed instructions.
You are an expert in vector-art image analysis and description. You will be given vector illustration, clipart, doodle.
Task:
Produce +*detailed, objective caption (30-80 words, 1-2 sentences) that precisely describes the visible content and the vector graphic structure.
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Must include, a consistent order:
1) Main subject (8) category, count, pose/action, relative sizes.
2) Appcarance details: sex, salient parts (clothing, accessories, facial/ hair features if present), simple shapes used.
3) Geometry line work: primitives (circlees/recttaglees/aaass curvature
corner roundness, symmetsy/repetitionn, perspective (front/side/
isometric), stroke style (thin/medium/thick solid/dashed, round/ square caps and joins).
4) Color fill: dominant palette names, fill types (#lat/gradient/ patterm/transparent]
5) Composition layout: spatial relations (teft//rigggtcceett////reeee occlusion), alignment, and layering depth if evident.
5) Text: if there is text in the image, transcribe visible words/ letters and note font feel (bloxk/srtipr//aadiiieee
case.
Constraints:
Be factual and visual only. No symbolism, history, brand identification or guesses about identity emotions.
Avoid opinions and storytelling; avoid tool/author speculation.
Prefer concrete nouns, numbers, and measurable attributes.
Remember to only output 80-80 words, 2-3 sentences.
Do not wrap the output in quctatien marks or triple backticks (1)).
Output format: e.g:
The illustration shows one woman in side view bending forward to place an arrow onto circular target symbol beside the large block letters "SEC." " She wears yellow short-sleeve top, purple pants, and green shees, with shoulder-length black hair. The vector uses clean curves, rounded edges, flat fills in purple, yellow, green, and skin tones, and medium-weight solid strokes. The composition places the letters the left, the target in the center, and the woman leaning in from the right, with light purple clouds above.
The illustration depicts a single serving of chocolate rousse an orange cup, topped with swirl of cream. The mousse is centrally placed on a beige plate. The vector art uses smooth, rounded shapes and flat fills orange, brown, beige, and cream, with no visible strokes. The composition is symnetrical, with the cup and plate aligned centrally, creating balanced and cohesive layout.
The illustration features a single woman on the right, gesturing toward key and lock, She has long dark hair and wears yellow top with green trim and white patterns. The vector employs smooth, rounded shapes and flat fills in yellow, green, purple, and black, with medium solid strokes. The key and lock appear in apeech bubbles on the left, using simple geometric forms like circles and squares, arranged asymmetrically.
C.4.2 QUESTION TEMPLATE
For the SVG description task, we randomly select one from the following five prompts the ques- tion input.
"(svg) VnDescribe in detail the semantic and geometric features of the svG code. ",
"Here is an SVG code snippet: An (avg) AnBased solely on this code, explain what the SVG code represents and describe its semantic meaning and geometric characteristics in detail.",
"The SVG scurce is given relow:\n(see) AnIdentify fy the main object depicted and 1ist its geometric shapes (e.g., reetangles, circles, lines) well as semantic cues (e.g., what the object is, its context, implied function)."
"Below is the full SVG code:An UnDescribe both the visual geometry (shapes, symmetry, layout) and the conceptual meaning (what represents
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symbolic features, color implications) the resulting image.", "svG Code: An (svg) AnWhat object dces this render? Provide detailed breakdown of its semantic purpose and all notable geometric features (o.g., angles, curves, proporticns)."
For multiple-choice QA tasks, we adopt the following template as the question input.
"As SVG specialist, you have deep knowledge of vector graphics and their properties. Given an SVG code snippet, analyze the code to answer the following multiple-choice question about the visual elements it represents. Provide only the final answer in the format 'A.', 'B.', (c.', or 'D.' (e.g., 'A."), without any additional explanation or text. (SVG)"
For the SVG editing task, we design a set of instruction templates that guide models to modify the original SVG code with minimal changes while preserving its structural integrity.
"You precision SVG editing engine. Your task is to medify the provided SVG code based on the user's s instruction. Follow these rules strictly:
1. Make only the minimal necessary changes the SVG satisfy the instruction.
2.
Preserve the original code structure, IDs, and classes whenever
possible.
3. Your output MUST be the complete, raw SVG code and nothing else. Do not include explanations, comments, markdown fences (like ''svg). SVG Code: (svg)
rustriction: (instruction)",
"You automated SVG medification utility. Your function is alter the given SVG code according to the instruction. Adhere to these diretivees: apply only the absclute minimum changes required. Do not alter existing IDs, classes, the overall structure. The output must be the raw, modified SVG code exclusively. Suppress all explanatory text, commentary, and markdown formatting. SVG Code: (svg) ructioon: (instruction)",
"You specialized SVG editing assistant. need you edit the SVG code below based on my instructicn. Please adhere to the following strict requirements:
1. Implement the most efficient and minimal change pessible.
2. Do not refactor reformat the code; preserve its original structure and attributes.
3. Your entire response must be the edited SVG code and nothing more. Source SVG: (svg) Editing Instruction: (instruction)",
"Act as an expert SVG code manipulator. Your objective to edit the following SVG to match the user's request.
**Modification Principle:** Edit only what is necessary to fulfill the instruction.
**Structural Integrity:** Maintain the original svG's s structure, including all IDs and classes.
+uOutp 4 Respond with only the final, raw SVG source code. No extra text, comments, or markdown. SVG Code: (svg) Instruction: (instruction)".
"As professional svG graphic editer, your job is to apply the requested change to the svG code.
Core Di Direttives:
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1. Change only what the instruction requires.
Keep the original code's s formatting and structure intact.
3. output the full, edited sVG code.
SVG to Edit: (svg)
User Request: (instruction)
REMINDER; Your response must not centain any text besides the SVG code itself.",
"Edit the SVG based on the instruction. Apply minimal changes, preserve the structure, and output only the SVG code.
SvG: (svg)
Instruction: (instruction)",
For the Text-to-SVC generation task, we design multiple instruction templates for the dialogue data, as illustrated below. These templates guide the model to produce SVG code that fulfills the given instruction, thereby enhancing the diversity of dialogue formulations.
"You are an expert SVG generation. After receiving an instruction, output an SVG that satisfies the instruction. Instruction: (instruction)", "You are skilled SVG designer. Based the instruction below, create an SVG that fulfills the given instruction. 'n Instruction: linstruction )", "You are a professional in vecter-based image generation. Please produce SVG that matches the following instruction.
inctrintoon: (instruction)",
"You are an expert in SvG generation. Generate sVG code that reflects the instruction.
Instruction:
(instruction)",
"As an expert in SVG generation, you will be given instruction. Please generate an SVG that satisfics the instruction.
Instructicn: (instruction)",
"You are a specialist in creating scalable vector graphics. Please design SVG that meets the requirements specified in the instruction. Instruction: (instruction)",
"You are an SVG development expert. Your task is to create an svG inage that accurately represents the given instruction. Instruction: (instruction)",
"You a master SVG generation. Please generate SVG file that fulfills the following instructicn requitements. rustiioionn (instruction)",
"You are a professional SVG graphic designer. Create an sVG that perfectly matches the provided instruction.
Instruction: (instruction)",
"You are an expert in vector graphics and svG coding. Please produce an SVG that satisfies the instructicn below. Instruction: (instruction)*
For the Image-to-SVG generation task, also design of dialogue templates. In these tem- plates, the model receives both an input image and textual instruction, and required to generate SVG code that fulfillss the given instruction.
"You are an expert in svG generation. After receiving an image and an instruction, output an SvG that satisfies the instruction. Image: <image>
Instruction ion: (instruction)", "You are a skilled SVG designer. Based on the image and instruction below, create svG that fulfills the given instruction.
Image: <image>
Instruction: (instruction)",
"You are a professional in vector-based image generation. Please produce an sVG that matches the following image and instruction. Image: <image>
instruction: (instruction)",
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"You expert in SVG generation. Generate SVG code that reflects the image and instruction.
Image: <image>
Instruction: (instruction)",
"As an expert in SVG generation, you will be given an image and an instruction. Please generate sVG that satisfies the instruction. Image: <image>
Instruction: [instruction)",
"You are a specialist in creating scalable vector graphics. Please design SVG that meets the requirements specified in the instruction.
Image: <image>
Inst icn: (instruction)",
"You are an SVG development expert. Your task is to create an SVG inage that accurately represents the given image and instruction. Image: <image>
Instruction: (instruction)",
"You are a master of sVG generaticn. Please generate an sVG file that fulfills the following image and instruction requirements. Image: <image>
Instruction: (instruction)",
"You are a professional SVG graphic designer. Create an SVG that perfectly matches the provided image and instruction.
Image: <image>
Instruction: (instruction)",
For the Text-to-AANi animation generation task, we design multiple dialogue templates guide the model in generating SVG animations from textual instructions.
"You are an expert in SVG animaticn creation. Given an instruction, generate SVG animation that fellews it.
Instruction: (instruction)",
"You are a skilled SVG animation designer. Based on the instruction below, produce an SVG animation that meets the requirement. Instruction: (instruction)",
professional in vector animation. Please generate SVG "You according to the fol following instruction.
animation Instructions (instruction)>
"You are an expert in SVG animaticn coding. Create svG animation code that fulfills the instruction.
Instruction: (instruction)",
"As an expert SVG animation, you will be given an instruction. Please output an SVG animation that matches it.
Instruction (instruction)",
For the Video-to-SANI animation generation task, design diverse set of dialogue templates to enable the model to generate SVG animations conditioned on both a video input and a textual instruction.
"You are expert in SVG animation creation. Given vi dride and instruction, generate SVG animation that follows it. Video: <video>
Instruction: (instruction)", "You are a skilled SVG animation designer. Based on the video and instruction below, produce an svc animation that meets the requirement.
Video: <videc>
Instruction: (instruction)",
"You are professional in vector animation. Please generate an SVG animation according to the follewing video and instruction.
Video: <video>
Instruction: (instruction)",
"You are an expert in vector animation. Given the video and the instruction, create an SVG animation that matches the request. video: <videc>
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Instruction:
(instruction)",
animation design. Use the video and instruction provided to generate an svG animation that aligns with them. Video: <video>
"You are a specialist
Instructien:
(instruction)",
"As professional SVG animation designer, analyze the video and instruction, then generate an sVG animation that fulfills the task. Video: <video>
Instruction: (instruction)", "You
expert in animated vecter graphies. Create SVG animation that reflects both the video and the given instruction.
video: <videc>
Instruction ion: (instruction)".
"You are skilled SVG animaticn creator. From the follewing video and instruction, produce SVG animaticn that conveys the intended action. Video: <video>
Instruction: (instruction)",
"You are a professional vector animation generator. Given the video and instructien, output svG animaticn code that implements it. Video: <video>
Instructions (instruction)",
"As an SVG animation expert, your task is to transform the video and instruction into a corresponding sVC animation.
Video: <video>
Instruction: linstruction)",
D THE USE OF LARGE LANGUAGE MODELS (LLMs)
We used large language models (LLMs) assistive tools during the preparation of this work. Specif- ically, LLMs were employed for language polishing, LaTeX code editing, and debugging of prompts the dataset construction process. The authors take full responsibility for the content of the paper.
57


Reviews:
[{"review_id": "omQ47gbj8U", "reviewer": "Reviewer_42aJ", "summary": "This paper presents InternSVG, an integrated data–benchmark–model suite designed for unified modeling of Scalable Vector Graphics (SVG) tasks — encompassing understanding, editing, and generation. The work consists of three main components:\n1. SAgoge — a large-scale (16M samples) multimodal dataset for SVG tasks covering icons, illustrations, chemical diagrams, and animations.\n2. SArena — a standardized benchmark for evaluating understanding, editing, and generation performance across SVG domains.\n3. InternSVG — a unified multimodal large language model (MLLM) built on a ViT–MLP–LLM backbone, augmented with SVG-specific tokenization (including tag, attribute, and numeric tokens) and subword-based embedding initialization. It is trained in a two-stage curriculum from simple to complex SVGs.", "questions": "1. Could you provide more analysis or visualization to help understand how unified modeling leads to cross-task improvements? For example, have you examined whether representations learned from SVG understanding tasks transfer effectively to generation tasks (e.g., through probing, clustering, or layer-wise similarity analysis)? Such insights could strengthen the paper’s conceptual contribution beyond empirical results.\n2. While quantitative metrics are comprehensive (FID, SSIM, CLIP, etc.), have you conducted any human evaluation on the perceptual or semantic quality of generated SVGs? If not, could you comment on whether the model-generated outputs align with human preference or creative intent?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The work takes a holistic and unified approach to SVG understanding, editing, and generation — previously treated as isolated problems.\n2. The paper is clearly written and well-organized, with informative figures illustrating datasets, architecture, and tokenization.\n3. The open dataset and benchmark will likely become an important reference for future research on vector graphics and multimodal LLMs.", "weaknesses": "1. While the paper demonstrates strong empirical results, it provides limited theoretical or interpretive analysis of why unified modeling helps cross-task transfer. For example, a visualization or probing analysis of SVG token embeddings could strengthen understanding of learned representations. \n2. The annotation pipeline leverages GPT-4o, Gemini, and other proprietary models for labeling, which may raise concerns about the bias. It would be helpful to involve human evaluation on a small subset of the synthetic data. \n3. While quantitative metrics are comprehensive, qualitative results (e.g., in Figure 4) could be expanded to better illustrate failure cases or tradeoffs between semantic accuracy and geometric precision.\n4. It would be helpful if the cross-domain transfer (e.g., training on icons → testing on scientific diagrams) could be further analyzed to highlight the generalization strength.\n5. The authors mention replacing unrenderable SVGs with black images for evaluation. It would be helpful if the authors could quantify how often InternSVG produces invalid SVGs compared to other baselines. This would clarify the model’s robustness and syntactic correctness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mtfrNHIJsk", "reviewer": "Reviewer_gzZQ", "summary": "This paper presents the InternSVG family, a unified suite that integrates dataset, benchmark, and model for SVG tasks. It introduces SAgoge, a large-scale multimodal SVG dataset with 16 million samples; SArena, a unified benchmark covering understanding, editing, and generation; and InternSVG, a MLLM designed for unified SVG modeling. Extensive experiments show that InternSVG achieves state-of-the-art results across multiple SVG-related tasks.", "questions": "See Weakness", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The work establishes a coherent and large-scale suite for SVG research, combining SAgoge and SArena to support consistent training and evaluation across understanding, editing, and generation tasks.\n2. The model employs SVG-specific tokenization and a subword-based initialization strategy, together with a two-stage training scheme that reduces sequence length, stabilizes optimization, and speeds up convergence.\n3. Comprehensive evaluations demonstrate that InternSVG achieves the best performance on all SArena sub-tasks, surpassing strong proprietary models such as GPT-4o and Claude-4-Sonnet.\n4. The paper is well organized and easy to follow.", "weaknesses": "1. Since SAgoge’s annotations are generated using InternVL and QwenVL, which are also included as baselines, it remains unclear whether this setup introduces a bias favoring models from the same family during evaluation.\n2. InternSVG is trained on large, SVG-specific data, whereas other models are used without such domain fine-tuning, making it unclear whether the gains arise from architectural advances or from data advantages.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9hzjPjOCVi", "reviewer": "Reviewer_5t8t", "summary": "This paper focuses on the tasks of Scalable Vector Graphics (SVG) understanding, generation, and editing. To address the limitations of prior works on SVG tasks, the paper proposes (1) a new large-scale and diverse SVG dataset SAgoge, (2) a new benchmark SArena with comprehensive tasks for SVG understanding, generation and editing and (3) a specialized MLLM: InternSVG for SVG tasks, built upon pre-trained VLMs with proposed special tokens and a two-stage training strategy. Experimental results demonstrate that InternSVG achieves superior performance on various SVG tasks.", "questions": "For the SVG understanding task, have the authors considered or conducted experiments comparing the SVG-based understanding with the rendered image/video based understanding?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "* The unified modeling of SVG understanding, editing, and generation is an interesting and important problem, since there may be various practical applications, while the problem is still underexplored.\n* The paper provides a comprehensive contribution to SVG tasks, with an integrated data-benchmark-model suite.\n* There are thorough experiments on the newly introduced SArena benchmark, and the performance of the proposed InternSVG model outperforms both existing SVG methods and various MLLM models.", "weaknesses": "* The experiments in the main paper are conducted primarily on the proposed SArena benchmark. Given that SArena is developed in alignment with the training data SAgoge (probably the same data source & similar task definitions), the comparisons are not fair. More experiments on other existing benchmarks would be necessary to fully validate the effectiveness of the proposed data & methods.\n* The illustration of the dataset / benchmark construction pipeline in the main paper is too brief, making it difficult to assess the methodological novelty or quality of the pipeline. And some details are not clear, e.g. how the annotations of the SVG editing task are obtained?\n* Some ablations are missing. The paper proposes special tokens and an embedding initialization strategy for InternSVG training. But there is no ablation study about the impact of these designs on the task performances.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vBmtQ4TKXH", "reviewer": "Reviewer_rkyc", "summary": "This paper aims to address the challenges of fragmented datasets and poor method transferability in the current field of SVG modeling. To this end, the authors propose a comprehensive solution named \"InternSVG,\" which consists of three parts:\n1.  **SAgoge**: A large-scale (over 16 million samples) and comprehensive multimodal SVG dataset.\n2.  **SArena**: A corresponding standardized benchmark for evaluation.\n3.  **InternSVG**: A unified Multimodal Large Language Model (MLLM), which is based on the mainstream ViT-LLM architecture and optimized with methods such as SVG-specific tokenization and a two-stage training strategy.", "questions": "1.  Regarding synthetic data bias: Have the authors analyzed potential issues of pattern repetition or stylistic homogenization introduced by the synthetic data pipeline (e.g., using FLUX and Claude models)? Is there a noticeable gap in complexity or creativity when comparing this synthetic data to real, human-authored SVGs?\n2.  Regarding SVG code quality: Besides visual evaluation, have the authors considered introducing metrics to assess the quality of the generated SVG *code* itself? For instance, code length (token count), path complexity, or the number of nodes. This would be very important for measuring the model's efficiency and the editability of the generated content.\n3.  Regarding the challenges of the \"style transfer\" task: The paper mentions that the model's performance on the \"style transfer\" task is slightly inferior to the strongest baseline. Could the authors provide a specific analysis of the unique challenges this task presents to the model, which makes it difficult for the current unified training framework to handle perfectly?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  **Dataset and Benchmark Contribution**: The most prominent strength of this paper is the creation of the SAgoge dataset and the SArena benchmark. This is a tremendous engineering contribution that provides an extremely valuable resource to the SVG research community. The scale, diversity (especially the inclusion of chemical structures and animations), and unified coverage of understanding, editing, and generation tasks in SAgoge fill a significant gap in the field.\n2.  **Effectiveness of Unified Modeling Validated**: The paper clearly argues for and experimentally demonstrates the advantages of unified modeling. By jointly training on understanding, editing, and generation tasks within the same model, the model achieves better generalization and performance.\n3.  **Comprehensive Experimental Evaluation**: The authors conducted a series of thorough experiments, comparing their method against a wide range of baselines, including traditional methods, general-purpose MLLMs, proprietary models, and specialized SVG generation models. The InternSVG model achieved state-of-the-art (SOTA) performance on their proposed SArena benchmark, which fully demonstrates the effectiveness of their method suite.", "weaknesses": "1.  **High Reliance on Synthetic Data**: A large portion of the illustrations and animations in the dataset are synthesized using other generative models (e.g., FLUX, Claude), and the annotations rely on automated processes (e.g., GPT-4o). This poses a potential risk: the dataset may contain systematic biases or stylistic limitations originating from these generation/annotation models, and may not fully reflect the diversity and complexity of SVGs created by human designers in the real world.\n2.  **Insufficient Evaluation of Generated Code Quality**: The paper's evaluation metrics primarily focus on the post-rendering visual effects (e.g., PSNR, SSIM, FID). However, as a vector format, the quality of the SVG code itself—such as conciseness, structural soundness, and editability—is equally crucial. The paper lacks a direct quantitative evaluation of the quality of the generated code. It is possible that the model generates visually correct but redundant and hard-to-maintain SVG code.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)