OmniSVG:  A Unified Scalable Vector Graphics Generation Model
Authors: Yiying Yang
Wei Cheng
Sijin Chen
Xianfang Zeng
Fukun Yin
Jiaxu Zhang
Liao Wang
Gang YU
Xingjun Ma
Yu-Gang Jiang
Venue: NeurIPS 2025 poster ()
Keywords: SVG Generation
Vision Language Model
Primary area: applications

Abstract: Scalable Vector Graphics (SVG) is an important image format widely adopted in graphic design because of their resolution independence and editability. The study of generating high-quality SVG has continuously drawn attention from both designers and researchers in the AIGC community. However, existing methods either produces unstructured outputs with huge computational cost or is limited to generating monochrome icons of over-simplified structures. To produce high-quality and complex SVG, we propose OmniSVG, a unified framework that leverages pre-trained Vision-Language Models (VLMs) for end-to-end multimodal SVG generation. By parameterizing SVG commands and coordinates into discrete tokens, OmniSVG decouples structural logic from low-level geometry for efficient training while maintaining the expressiveness of complex SVG structure. To further advance the development of SVG synthesis, we introduce MMSVG-2M, a multimodal dataset with two million richly annotated SVG assets, along with a standardized evaluation protocol for conditional SVG generation tasks. Extensive experiments show that OmniSVG outperforms existing methods and demonstrates its potential for integration into professional SVG design workflows.

OmniSVG: A Unified Scalable Vector Graphics Generation Model
Yiying Yang!
Wei Cheng
Sijin Chen' Xianfang Zeng"
Fukun Yin 1.2
Jiaxu Zhang?
Liao Wang
Gang Yu²
Xingjun Ma
Yu-Gang Jiang'
Fudan University
StepFun
Project Page
MMSVG-2M
MMSVGBench
Models
Code
Text-to-SVG
Icon
Illustration
Character
dhatt wich reuie
Image-to-SVG
Character Reference
Samples Generated by OmniSVG with Wide Complexity Range
Versatility
Figure OmniSVG is capable autoregressively generating high-quality Scalable Vector Graphs (SVG) across a wide spectrum of complexity, from simple icons to intricate anime characters. OmniSVG demonstrates remarkable versatility in generating high-quality SVGs adhering multimodal instructions, covering tasks like Text-to-SVG. Image-to-SVG, and Character-Referencee SVG, making powerful and flexible solution for diverse creative tasks.
Abstract
Scalable Vector Graphics (SVG) an important image format widely adopted in graphic design because of their resolution independence and editability. The development of autonomous SVG generation workflows is continuously drawing attention from both designers and researchers in the AIGC community. However, existing methods either produce unstructured outputs at huge computational cost are limited to generating monochrome icons of over-simplified structures. To produce high-quality and complex SVG adhering to multi-modal instructions, we propose OmniSVG, unified SVG generation framework that inherits knowledge from a pre-trained Vision Languuge Model (VLM). By parameterizing SVG com- mands and coordinates into discrete token sequences, the auto-regressive nature enables us to scaminglessly adapt modern VLMs to the direct SVG generation. To further advance the development of SVG synthesis, a multimodal dataset with two million richly annotated SVG assets, along with a standardized evaluation protocol for conditional SVG generation tasks. Extensive experiments show that OmniSVG outperforms existing methods and demonstrates potential for integration into professional SVG design workflows.
introduce MMSVG-2M,
Yiying Yang and Wei Cheng contributed equally to this work. Corresponding Authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Scalable Vector Graphics (SVG) have become a cornerstone of modern digital design because of their resolution independence, compact file size, and inherent editability. Widely adopted in professional workflows from UV/UX design to industrial CAD systems, SVG enables precise manipulation of geometric primitives (e.g., Bézier curves, polygons) while maintaining high precision and consistent visual quality across varying resolutions. However, creating high-quality SVG content remains challenging for non-experts, requiring mastery of specialized tools or intricate XML syntax.
Existing methods adopt either optimization-based methods or auto-regressive approaches generate SVG contents. The optimization- based methods [24] [29] iteratively refine the SVG parameters by minimizing the differences between the input image and the raster image created by differentiable vector graphics rasterizers. Though these methods are sufficient for reconstructing SVG icons, they suffer from significant computational overhead when scaling up to more intricate samples and produce unstructured outputs with redundant anchor points, harming the editability of the reconstructed SVG samples. In contrast, regr-sseress methods build transformer models or adapt pre-trained Large Language Models (LLMs) to directly generate XML parameters [59] or codes [56 42 representing
SVGs. Benefiting from the end-to-end learning pipeline, the auto-regressive method a more scalable approach 5 as it can learn directly from a large collection of SVG samples. However, existing
auto-regressive approaches are limited basic SVG contents 11 24 because of the limited
context length and the scarcity of complex SVG data.
In this paper, we propose OmniSVG that hamnesses native VLMs for various end-to-end mul- timodal SVG generation tasks. By parameterizing SVG coordinates and commands into discrete tokens, OmniSVG decouples structural logic from low-level geometry, mitigating the "coordinate hallucination" problem prevalent in code-based LLMs, and produces vivid and colorful SVG results. Additionally, the next token prediction training objective enables OmniSVG to complete SVGs with diverse generation results given some partial observations. Compared to traditional auto-regressive SVG generation methods, OmniSVG is able to parameterize SVGs exceeding 30k tokens, facilitating the generation of detailed and complex SVG contents. Building upon pre-trained VL.Ms, our method natively integrates the ability to reason upon visual and textual instructions to synthesize editable, high-fidelity SVGs across diverse domains, from icons to intricate illustrations and anime characters.
To advance the development of SVG synthesis, we introduce MMSVG-2M, a multi-modal SVG synthesis dataset with two million richly annotated assets, encompassing icons, illustrations, and anime designs. We also establish a standardized evaluation protocol, MMSVGG-Bench for "Text-to- SVG" and "Image-to- SoVS"" generation. Extensive experiments show that OmniSVG can produce highly detailed and complex SVG contents, surpassing prior art both quantitatively and qualitatively.
To summarize, our key contributions include:
We introduce OmniSVG, a family of end-to-end multimodal SVG generators that leverage native VLMs for generating complex and detailed SVGs, from simple icons to intricate anime characters.
We present MMSVG-2M, a large-scale dataset comprising two million SVG assets, along with standardized evaluation protocol for various multi- modal SVG generation tasks, providing a comprehensive resource for future research.
Extensive experiments show that OmniSVG surpasses prior SVG generation methods both qualitatively and quantitatively, highlighting its potential for integration into professional SVG design workflows.
2
Related Works
SVG Generation. Early attempts to generating SVGs directly utilize architectures like RNNs 18
[T) and Transformers 4 to compress SVG commands into latent 19 44 45), VAEs [4 48
representations. Meanwhile, DeepSVG [4] further parameterizes SVGs using a dual transformer architecture but struggles with geometric consistency. Recently, the advent of large language models
(LLMs) (30 64
52 61
49 unleashes the potential of generating SVGs via XML code However, the limited context length of existing L.LM-based SVG generation poses significant challenges in handling complex SVGs that exceed 10k tokens.
63
synthesis [59 56 42
methods [56 42 59

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In this paper. we explore the potential of native Vision-Language Models (VLMs) in multi-modal SVG generation. By combining pre-trained VLMs with SVG command parameterization, we validate that OmniSVG is able to follow multi-modal instructions and generate vivid and complex SVGs.
Image Vectorization. Recent advancements in vectorization harness diffusion models paired with differentiable rasterizers, using techniques like score distillation sampling [37] [22] 7 and specialized regularizers [29 34] convert raster images into SVG paths. While these methods achieve remarkable results, they face limitations such as over-smoothing, color over-saturation, and lack of editability, often producing tangled paths that fail to capture hierarchical structures inherent in professional SVG designs. In this paper, we present an end-to-end approach that follows multi-modea instructions to generate high-quality SVGs with improved path clarity and editability.
SVG Datasets and Benchmarks. The lack of suitable datasets for complex SVG structures presents significant challenge. Existing datasets [11
primarily focus on simplified path-based SVGs or monochrome icons, overlooking the intricate layered structures and rich color semantics found in real-world designs. For example, FIGR-8-SVG [I]] focuses on monochromatic icons, while StarVector [42) proposes categorized datasets, including illustrations, icons, emojis, and fonts. Therefore, existing datasets only present simple SVG samples that do not exceed 8.2k: tokens, failing to capture the complexities of layered structures and rich color semantics. Benchmark evaluations, such as VGBench [70]. further highlight gaps in multi-format testing and the absence of comprehensive coverage for illustrative SVGs. To this end, we introduce MMSVG-2M, multimodal SVG synthesis dataset comprising two million richly annotated assets, including icons, illustrations. and complex anime designs. We also present standardized evaluation protocol, MMSVG-Bench, to evaluate diverse multi-modal SVG generation tasks with varying complexity.
24 [3
3 OmniSVG Dataset
We present MMSVG-2M, large-scale SVG dataset with two million SVG samples covering website icons, illustrations, graphic designs, anime characters, and etc (Sec.3.. (.T). To promote the downstream development of SVG generation methods, we also introduce MMSVG-Bench, standardized evaluation protocol for a series of multi-modal instruction following tasks for conditional SVG generation (Sec. 3.2).
3.1 MMSVG-2M
Data Source. With increasing visual complexity, MMSVG-2M consists of three subsets, 1) the icon subset MMSVG-Icon collected from Iconfont, 2) the illustration subset CeSVV--llusstation sourced from IconSount, and 3) the complex anime character subset MMSVG SMMSG-Character both curated from Freepik and created by data creation pipeline shown in Fig. 2 All these websites online platforms where users can publish and share SVGs, encompassing a broad variety of categories. Specifically, our collection of MMSVG-2M contains 1.1 million icons, 0.5 million illustrations, and 0.4 million anime characters as shown in Tab. 6
Data Curation. We extract SVG samples with a comprehensive deduplication process based on filenames, SVG code, and metadata. We first fit the collected SVGs within a viewbox of 200 x 200. Then, we employ an off-the-she VLM, specifically BLIP-2 (28. to generate captions for the SVGs. Please find more samples from the MMSVG-2M, dataset in Fig. g and instruction templates in Appendix A.2
SVG Simplification is an essential procedure in SVG data cleansing, since the over-complicated XML grammars in the crawled SVG data will lead to ambiguities while representing basic shapes. To standardize training and evaluation, we simplify all SVG commands with atomic commands as shown in Tab. I Inspired by FIGR-8-SVG [T]] and IconShop (57]. we remove all attributes and simplify each SVG with five basic commands, including "Move To" (M), "Line To" (L), "Cubic Bézier" (C), "Elliptical Are" (A), "ClosePath" (Z). The introduction atomic commands further removes the ambiguities, complex XML grammars can be approximated with the combination of several atomic commands. To efficiently produce a unified and less complex data structure, we utilize picosvg too remove grammars like "group" and "transform". and simplify the complex commands
3

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Table 1: SVG Draw Commands. Draw commands used this work along their arguments and Visualization are listed. The start-position (æ), y1) is implicitly defined as the end-position of the preceding command.
Command
Arguments
Description
Visualization
<SOP>
Ø
"Start-of-Paaee token.
Move the cursor the end-point (222, V/) without drawing anything.
(Pa)as)
22, 92
(MovcTo)
T2, 1/2
Draw a line to the point (2))
(xx.9t)
(LineTo)
C (Cubic Bézier)
Gai Qy1
Draw a cubic Bézier curve with control points (g1). (4a2, Gy2) and end-poin sol-poont (22, V2).
Gxu. Gy2
Z2, y/
(purda))
(sta 3b)
Fyy sp. f fs 22. M2
Draw an elliptical are with radii y, and Ty (semi-major and rotated by angle yp to the x-axis, and end-point (I22, 12). (122,
(Elliptical Arc)
axes).
Close the path by moving the cursor back to the path's 's starting position (Ia,
(ClosePath)
F (Fill)
fill
Draw the fill attribute of the path.
<EOS>
'End-of- of-SVG token.
to atomic path commands. It is worth noting that atomic path commands are sufficient to represent complex SVGs shown in Fig.
3.2 MMSVG-Bench
To compensate for the vacancy of standardized and open evaluation for SVG generation, we introduce MMSVG-Bench, comprehensive benchmark for multi-modal SVG generation. We require the corresponding benchmark to be sufficient verification whether model is practically useful in real-world scenarios, and avoid the excessive similarity between the benchmark input data and training data as in traditional train/test splits. Therefore, we opt to generate the benchmark inputs with GPT-4o. Specifically, for Text-to-SVG task, we synthesize 150 textual prompts for each SVG subset (i.e. Icon and Illustration). For Image-to- toGGG task, we synthesize extra 150 textual descriptions, and prompt GPT-4o to generate veclor-style images with transparent backgrounds based on the above texts as the ground truth visual samples. We focus on both the visual quality and semantics of the generation results.
Text-to-SVG requires model to generate SVGs from text instructions. We measure the visual quality with Frechet Inception Distance (FID) [50), aesthetic appeal with Aesthetic score 43], text-SVG alignment with CLIP score [88 and Human Preference Scores (HPS) (58).
Image-to-SVG evaluates a model's ability to convert images into SVGs. To quantify the dis- tance between the input and output SVG, calculate the cosine similarity of DinoV2 features (DinoScore) 35 Structural Similarity Index (SSIM) (54), Learned Perceptual Image Patch Similarity (LPIPS) (66), and Mean Squared Error (MSE).
Character-Referencce SVG Generation evaluates model's ability to generate novel SVGs while keeping the profile of the characters depicted in the input image. Different from image-to-SVG, the model does not reconstruct, but generates a specific character SVG for the input image (see
Fig- 5)
We evaluate the alignment between input character images and generated SVGs by prompting
GPT-40
21 to generate score ranging from I to 10, the higher the better. [15 23 17

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Create of character
i
OmnisVG
mreen
Gwen2.5 5 VL
Dcto
SUC
Soy
Sleq
Btrt Part
Brdiof Rach
Gwen2 Vocabulary (150k)
Vocabulary (4Dk)
Vowe
Tokenizer
Image
Llaie bizarr
Convensio
MMSVG-2M Dataset
Image
Input
A
Viracer
Icon
S Illustration
Character
Figure 2: Overview of OmniSVG. OmniSVG is built pre-trained vision-language model Qwen2.5-VL and incorporates SVG tokenizer. model tokenizes both text and image inputs as prefix tokens, while the SVG tokenizer encodes vector graphics commands into a unified representation space.
OmniSVG
To support end-to-end training for multi-modal SVG generation, OmniSVG parameterizes series of atomic SVG path commands into a sequence before feeding into a pre-trained VLM with multi-modal instructions.
SVG Tokenizer, As illustrated in Sec. 3 our MMSVG-2M dataset simplifies an SVG by removing all attributes and using five basic path commands (see Tab. [T] After the simplification. an SVG script G is represented as the combination of M paths, G (Pi Here, P. is the i-th path containing
N₂ commands, P. = {C: N Jj=1, where C/ is the j-th command in the i-th path. Each command is
represented C (U Vi'), containing both the command type identifier U/ E (M, C, Z} and the corresponding location argument V.d. To generate colored SVG contents, we assign special tokens for hex values to control the "Fill" (F)) attribute, distinguishing it from the original SVG commands and coordinates. To this end, are able to total six types of commands U. L, Z, F} to parameterize a colored SVG parameterization.
Specifically, our SVG tokenizer transforms SVG scripts X, into ordered SVG token sequence within the same representation space the pre-trained VL.M. Following IconShop (57), we flatten the layered structure of the SVG script by concatenating different paths into single command sequence, where each path begins with the drawing commands followed by point coordinates. Therefore, each SVG sequence could be represented special tokens like <SOP> and <EOS> to the two ends of SVG sequence, identifying the begining and ending of a SVG sequence. We assign special tokens for each command type, i.e. {M, L, C, A, Z, F}. To shorten the length of the SVG sequence, we further merge the 2D point coordinates into one token with mapping function: æ, y > w + y, where w is the width of the image. The SVG sequence are then lifted into the same embedding space as the pre- pre-trained VLM with a learnable embedding layer.
flattened sequence. As the generation identifier, apply
Model Architecture. OmniSVG adopts Qwen2.5-VV 1 an open-sourced VLM that excels at understanding intricate vision-text inputs, as its backbone (Fig. 2 to produce precise and compact SVG outputs. OmniSVG is trained to predict the SVG suffix tokens (x,) conditioned on the mutli- modal instruction prefix tokens (x.) with the standard next-token prediction objective.
g" arg max II P (I Fx,cis
(I)
im
5

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Table 2: Quantitative Evaluations. Quantitative results between OmniSVG and state-of-the-art text-to-SVG and image-to-Si baseline methods. bold numbers underlined numbers represents best and second best performance repectively. Our VG model demonstrates superior performance compared SOTA SVG generation baselines.
Methods
Text-to-SVG
Inage-to-SVG
FID
CLIP1
Aesthetict
HPS1
DINOT
SSIMT
LPIPS
MSEJ
Vectorfusion
SVGDreamer
132.0k
308.94
0.207
4.26
0.221
Cha2SVG
0.6k
190.87
0.299
4.41
0.247
lconShop
37
2.0k
213.28
0.288
4.55
0.244
LIVE
52.5k
0.932
0.943
0.106
0.011
DiTVG
322.0k
0.940
0,954
0.066
0.002
GPT-4o 21
0.3k
0.860
0.792
0.403
St)
+
2.0k
0.895
0.881
0.231
0.059
Vtracer
52.4k
0.993
0.966
0.039
0.002
OmaiSVGGIIBB
3.8k
137.40
0.275
4.62
0.244
0.993
0,950
0.050
0.006
OmaiSVG-L/SB)
5.7k
130.56
0.276
4.60
0.242
0.922
0.893
0.235
0.040
Vectorfusion
23
66.1k
253,94
0.185
4.94
0.226
SVGDreamer
60
132.0k
419.70
0.201
4.37
0.221
Cha2SVG
1.0k
0.283
leonShop
X7
2.6k
107.93
0.233
4,46
0.224
LIVE 14
2k
0.935
0.950
MMSVG-Illsstration
DifVG 70
322.0k
0.945
0.955
0.065
0.001
GPT-4o
21
0.4k
0.875
0.854
Staz Nectorro)
72
2.6k
0.877
0.900
0.238
0.046
Viracer
0.994
OmmiSVGGr1BB)
3.8k
154.37
0.226
4.56
0.232
0.899
0.906
0.237
0.034
OmmiSVG-LISBB
138.42
0.231
0.905
Experiments
To validate the effectiveness of our method, we first introduce the baselines (Sec. 5.1 Then, we make quantitative comparisons with prior arts (Secs. 5.2 and 5.3) and conduct ablations (Sec. 5.4) to study the effectiveness of our design.
5.1 Baselines
For the text-to-SVG task, we compare our method with language-based (LLM-based) methods, includ- ing VectorFusion (2), SVGDreamer [60], Chat2SVG (56) and IconShop [57). For image-to-SVG task, we compare our method with baseline methods across image vectorization and Multimodal Large Language Modeling approaches, including LIVE (34), DiffVG [29), Star Vector
(42), Vtracer [12] and
GPT-4o [2 using the official implementations with the hyperparameters proposed by the authors, and apply their pre- and poot-procsssing code required.
5.2 Quantitative Comparisons
We compare our OmniSVG with other baseline methods on the "text-to-SVG" and "image-to-SVG" tasks in our MMSVG-Bench. addition to the metrics mentioned in Sec. 3 we also report the average token length (# tokens) of generated SVG sample utilizing the Qwen2.5-VL [[] tokenizer.
As shown in Tab. 2 OmniSVG demonstrates strong performance compared state-of-the-art baselines in text-to-SVG generation, achieving superior FID scores and competitive CLIP score, aesthetic quality, and HPS. For Image-to-SVG. OmniSVG also achieves competitive results with traditional vectorization methods, i.e. LIVE (34), DiffVG [2)], and VTracer [12), but with much shorter sequence length. When comparing to auto-regressive methods, i.e. GPT-4o (1] and StarVector [2). OmniSVG also achieves superior performance across all metrics. The above results indicate that OmniSVG effectively balances the generation cost and the visual quality when generating SVGs according to multi-modal conditions.

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Text Prompt
IconShop
SVGDreamer
VestarFusion
Chas25VG
Text Prompt
IconShop
SVGDreamer
VectarFusion
Chat25VG
Ours
Ours
reggiifyng decament
bess
Icon
ddlendar with
A par of 10 glesues
batterfly shope
drout board dip symbol
mitimalerr plant growing
condle matting
Illustration
laptop connected by goowing lines
A oup tee lamen sliess and herlss
intoracting friendyy
Ieaadd with free
Figure 3: Qualitative Comparison with SOTA Methods on Text-to-SVG Task. We compare the propose method with SOTA Text-to-S SotG methods on our evaluation benchmarks, namely Icon and Illustration.
5.3 Qualitative Evaluations
Text-to-SVG task. We compare method with baseline approaches using seven distinet text prompts for the teXt-to-SVG task, as shown in Fig. 4 Optimization-based methods like SVG- Dreamer [60] and VectorFusion [22] require significant computation time due to their iterative optimization processes, which, while effective for refining SVG details, are computationally ex- pensive. Auto-regressive methods, such as IconShop (57] and Chat2SVG (56), generate SVGs more quickly by leveraging pre-trained models but have notable limitations. IconShop produces monochrome SVGs, restricting its applicability, while Chat2SVG, though flexible, generates less detailed and semantically consistent SVGs in its first stage. Our OmniSVG consistently outperforms all baselines across various text prompts in generating high-fidelity SVGs with rich color, geometric accuracy, and the ability to handle complex visual cues.
Image-to-SVG Task. We compare our method with classical image vectorization approaches, including DiffVG [29]. LIVE [34, and VLM- based methods GPT-40 (21. Veator [42
CRef
Ours
CRef
Ours
and Vtracer 12 As shown in Fig. 4 our method outperforms these baselines in both quality and efficiency. Optimization-bann based methods like Dif- (VG and LIVE perform well simple icons but struggle with complex images, often generating visual artifacts. The GPT-4o model, while capa- ble of generating SVGs for complex images, is limited icon-level outputs and cannot handle detailed illustrations. Star Vector excels at sim- ple icons but fails to produce accurate SVGs for more intricate images, highlighting its limited generalization capability. Vtracer is an image processing algorithm designed to convert raster images into SVGs. contrast, OmniSVG effi- ciently converts wide range of images, from icons to complex illustrations and character images,
Figure 5: Generated SVG with Character-Referencc (CRef) by OmniSVG.
7

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Image Prompt:
Starvactor(BB)
Image Prompt
Starvactor(BB)
GPT-4o
DifNG
LIVE
Ours(BB]
GPT-4a
DiffVG
LIVE
Qurs(BB)
Icon
Illustration
X
Figure 4: Qualitative Comparison with SOTA Methods on Image-to-SVG Task. We compare the propose method with SOTA Image-to-SVG methods our evaluation benchmarks.
into high-quality, editable SVGs. This superior performance in handling diverse visual cues distin- guishes OmniS VG from traditional vectorization methods. Additional visual results can be found in Fig. [12 We provide more detailed discussions with existing methods, particularly the recent works
LLM4S VG [59] and Star Vector [42]. in the Appendix D
Character-Referencce SVG generation task. As shown Fig. by training on MMSVG- with natural character image and SVG pair data, OmniSVG is capable of generating character SVGs through image references.
5.4 Ablation studies
Effectiveness of SVG Parameterization. We present a comprehensive comparison among different SVG parameterization strategy with the traditional non- oon-parameterized methods for SVG represen- tation in large language models. We ablates on the parameterization on both coordinate and color attributes of the SVG.
The results, shown in Tab. 3 and Fig. demonsttate that parameterizing both coordinate and color attributes yields better generation results under all metrics with the shortest token length. It further validates that the efficient token representation allows our method to generate complex SVGs with fewer computational resources. Additionally, qualitative results show that our method outperforms others, particularly as SVG complexity increases. The poaapaaramttiizttioon method fails to generate
Table 3: Quantitative Study SVG Parameterization. Ablation studies color parametrization (abbreviated color param.) and coordinate paramterization (abbreviated as coord param.) are conducted.
Text-to-SVG
Image-to-SVC
Methods
# Tokens
FID
CLIP
Aesthetict
HPS
DINO!
SSIM LPIPS
MSE
W/o param.
218.76
0.185
3.43
0.138
0.741
0.718
0.315
0.182
18.5k
w/o coordinate param.
193.42
0.216
3.90
0.169
0.826
0.809
0.248
0.119
10.2k
w/o color param.
167.28
0.269
4.31
0.211
0.895
0.879
0.179
0.053
6.3k
OmniSVG(4B)
145.89
0.308
4.59
0.238
0.946
0,928
0.138
0.020
4.8k

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Table Ablation the Model Size. As the model size grows, the generated samples are of higher quality.
Text-to-SVG
Image-to-SVG SSIM1 LPIS
Methods
Input
Size
FIDL
CLIP1
Aesthetic
HPS1
DINO
MSEJ
FLAN--T--
[0]
Text
198.45 488
0.158
3.38
0.085
FLAN-T3-Large
10
Text
770M
175.24
0.208
3.92
0.142
FL
Text
160.28
0.258
4.31
0.192
blip2-finn-t5-x1
Tex//mmae
3,94B
152.11
0.235
4.48
0.215
0.898
0.891
0.255
0.041
OmniSVG(4BB)
Text/Image
3.7B
145.89
0.308
4.59
0.238
0.946
0.928
0.138
0.020
SVGs for complex images. These findings underscore the effectiveness of our full parameterization strategy in balancing performance and resource efficiency for SVG generation tasks.
Ablation studies model size. To analyze whether training a larger model benefits SVG generation, we evaluate Om base models with different sizes on the MMSVG-2M dataset in Tab. 4 We evaluate OmniSVG with base models of varying sizes on the MMSVG-2M dataset in Tab. 4 by progressively scaling up the model size. The results show that as the model size grows, we can generate SVG samples with a better quality.
Table Ablation on VLM architecture.
Tex--to-VG Aestheticf
Image-to-SVG
Vision Model
Language Model
FID
CLIP1
HPS
DINO1
SSIM
LPIPS
MSEJ
CLIP
Qwen2.5
185.31
0.249
4.52
0.215
0.867
0.856
0.267
0.058
VQGAN
Qwen2.5
198.74
0.234
4.49
0.203
0.839
0.828
0.295
0.071
145.89
0.M88
4.59
0.238
0.946
0.138
0.020
134.45
0.254
4.56
0.237
0.914
0.900
0.233
0.036
Ablation Studies on the VLM Architecture. To evaluate the effectiveness of the VL.M architecture, we conducted an ablation study replacing with alternative LLM-based architectures incorporating image encoders such as CLIP ViT-B/32 (39), VQGAN [14), and Owen2.55VL. 1.
The results in Tab. 5 show that Qwen2.5-VL consistently outperformed all alternatives under all evaluation metrics.
w/o color param.
w/o coard. param.
Prompt
w/ param.
full param.
User Study. We extract one-tenth of the sam- ples from the evaluation dataset and conducted a user study with 15 participants to evaluate user preferences, vividness, and the alignment between text-to-SVC and image-to-SVC -VG.. Par- ticipants are asked to assess SVGs generated by different models based on 150 text descriptions and 1.0 image prompts, comparing the results generated using our method and baseline mod- els. The results in Fig. show that OmniSVG is widely preferred, with higher scores for vivid- ness and superior semantic alignment with the input conditions.
selle green untridls.
with are
parks
6 Conclusions
Conclusions. We introduce OmniSVG, a uni- fied framework for multimodal SVG genera- tion that leverages pre-trained Vision-Language Models (VLMs). By parameterizing SVG com- mands and coordinates as discrete tokens, OmniSVG efficiently decouples structural logic from geometry. addressing issues like "coordinate hallucination" while maintaining design expressiveness. Our method outperforms existing approaches in both quality and efficiency, offering high-quality, editable SVG across various design domains. Additionally, we proposed MMSVG-2M. a large-scale multimodal dataset with two million annotated SVG assets and a standardized evaluation protocol. Extensive experiments show that OmniSVG surpasses prior SVG generation methods in various
Figure 6: Qualitative Study on Parametrization.

---

conditional generation tasks, highlighting its potential for integration into professional SVG design workflows.
Limitations and Future Work, During inference, OmniSVG generates tens of thousands of tokens for complex samples, which inevitably leads to considerable generation time. OmniSVG only bounded by vector style image prompt and fails on natural images. As for future work, recent endeav- ors multi-token prediction [3 and K KV-cache compression [68] 3 provide promising way to save the generation cost. Additionally, the auto- regressive nature of OmniSVG also unlocks future opportunities for in-context learning [67 [69] 47), chain-of-thougee reasoning [55]
Figure 7: User Study of OmniSVG and baselines.
Method
Preference
Vividity
Alignment
Vecterfusion
35
58
76
SVGDreamer
60
41
65
79
Chat2SVG
loonShop S7
57
75
GPT-4o 20
38
34
80
22
DaEVG
76
LIVE (4
36
70
95
OmniSVG
88
98
16 and multi-turn
interleaved generation
20
thereby providing a more precise user control.
Acknowledgements
This work is in part supported by National Key R&D Program of China (Grant No. 2022ZD0160103), National Natural Science Foundation of China (Grant No. 62276067), and National Natural Science Foundation of China (Grant No. 62472104).
The computations this research were performed using the CFFF platform of Fudan University,
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Appendix
A Additional Details of MMSVG-2M dataset
A.1 Samples of MMSVG-2M Dataset
visualize samples of our MMSVG-2M dataset in Fig. our MMSVG-2M dataset, 55% SVG samples belongs to the MMSVG-Icon, 25% belongs the MMSVG-Illustration, and the rest 20% belongs to the MMSVG- CMsSGG-hhaaattrr. Among the SVG samples within the MMSVG-Character category. half of them comes from Freepik, while another half is generated by our data creation pipeline. We also collect Iagge-SVG pairs for the character-reference SVG generation tasks during the generation process.
Table 6: Data Statisties for MMSVG-2M. Our MMSVG-2M consists of 1.1 million SVG icons, 0.5 million SVG illustrations, and 0.4 million SVG anime characters.
Dataset
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MMSVG-Icon CMsSVG-Iluusrattnn
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IconScout
8.1k 1 3.3k
MMMSVG-Character
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Freepik & generated
28k 1 7.3k
A.2 SVG-Image-Texx Pairs Construction
Our MMSVG-2M dataset comprises two million SVG samples with the corresponding rasterized images. We generate captions the rasterized images with BLIP-2 (28), thereby providing textual descriptions that enable us to fine-tune our model to follow these instructions. We use CairoSVG [25 for rasterization and remove samples that produced completely white images.
Annotation. We employ an off-the-shelf VLM, specifically BLIP-2 [28). to generate SVG captions with the prompt below. To reduce hallucinations, drop the samples with CLIP scores less than 30. We also visualize the distribution annotated keywords of MMSVG-2M dataset in Fig. 10 with word cloud format. And the instruction template for annotation is shown in Tab. 7
Instruction templates. MMSVGBench provides three tasks, including Sext-to-SVCl task, image- to-SVG task and character-referencce SVG generation task. Each task needs different instruction templates. For the text and image conditioning SVG generation, we provide the input text or image with VLM architecture. For character-referent SVG generation, provide the natural charecter
Instructions for Different Tasks
Employed BLIP2 for SVG Captioning: You are a helpful assistant. Your task to describe this image in single sentence, including the object, its color, and its overall arrangement, For example: "Yellow cheers with glasses of alcohol drinks." "Heart emojis represent love Valentine's Day."
Text-to-SVG: You are helpful SVG Generation assistant, designed generate SVG. We provide the text description as input, generate SVG based on the text.
Image-to-SVGG You are helpful SVG Generation assistant, designed to generate SVG. We provide image as input, generate SVG for this image.
Character-Refernne SVG Generation: You are helpful SVG Generation assistant, designed to generate SVG. We provide natural image as input, please generate the simplified character SVG based on the reference input image.
Table 7: Instructions for Different Tasks. Instructions including annotation, text-to-SVG, image-to-SVG and Charatter-refrennce SVG generation.
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MMSVG-2M-ccon
MMSVG-2M--Chrracter
55%
10%
10%
25%
MMSVVG-2M-lllusrrattin
MMMSVG-2M-Characca.
Figure 8: Samples from MMSVG-2M Dataset. The proposed MMSVG-2M dataset be separated into three subset, namely Icon. Illustration and Character. Samples from Icon, Illustration and part Character subsets are downloaded from Internet. Another part Character subset generated by our data creation pipeline, which can provide image and SVG pairs for image prompting task.
reference image and the original image with the VLM architecture. The list instruction templates for different tasks are shown in Tab. 7
A.3 Character-SVG Pairs Construction
As illustrated in the Fig. 6 part of our proposed MMSVG-2M-Charcee subset is constructed using a generative pipeline. As shown the pipeline diagram in Fig. 2 we employ FLUX (26 2baed generative model enhanced with vector-style LoRA to enable the generation of SVG-style data. For image-basee based conditioning, we adopt FLUX-Redux [27], which injects image features via a SigLIP encoder and projects them into image embeddings. These embeddings then concatenated with the text tokens as conditioning inputs for FLUX [26). However, in practice, the original Redux [27] variant
conditioning proves be overly strong. To address this, adopt community-implemennte of Redux that downsamples the image embeddings in 2D space. As observed in our experiments shown in Fig. a downsampling factor between 2 and 3x yields the most reasonable SVG-style character references. Finally, we employ VTracer 12 to perform near-instant vectorization of the generated images. To construct the MMSVG-22 Character subset, we first filter 103k character instances from the Danbooru [13] dataset and apply the aforementioned pipeline with motion and
expression keywords like previous works g 9 36 65). We compare the raw FLUX (26) outputs
and their vectorized counterparts, retaining only those samples with PSNR and SSIM scores above certain threshold as valid data.
B
Additional Details
B.1
Scaling Up
To study the effectiveness of scaling up multimodal SVG generation, we scale up OmniSVG from 4B to 8B parameters. We present training perplexity Fig. IT where both models are trained from scratch on 250 billion tokens. We show that, as the size of the model grows, the model achieves lower validation perplexity, indicating a higher probability of producing the validation data.
B.2 Implementation Details
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Minimalist
We train our models in 6 with the ZeRO- 2 strategy [40] for memory-efficient training. We also adopt the AdamW [3 optimizer with
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and weight decay of 0.1 to train our model. In practice, we load the pre-trained weights from the Qwen2.5-VL I1 model and initialize the SVG embeddings from scratch. Without further specification, we generate SVGs with the top-k and top-p sampling strategy with io = 50 and p 0.95 for diversity.
Retail
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C Additional Results
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As list in full comparisons in Tab. includ- ing all the baselines mentioned in Sec. For
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the text-to-SVG task, we compare our method
Figure 10: Word Cloud Visualization of Label Distri- bution in the MMSVG-2M Dataset. The size of each label corresponds to its frequency of occurrence, The larger the label, the more frequently it appears in the dataset.
with language-based (LLM-based) methods, in-
cluding VectorFusion 22 SVGDreamer [60], Chat2SVG [56] and IconShop [57]. For image- to-SVG task, we compare our method with baseline methods across image vectorization and Multimodal Large Language Modeling ap- proaches, including LIVE (34), DiffVG (29), Star Vector 442 and GPT-4o 21 using the official implementations with the hyperparameters proposed by the authors, and apply their pre- and post- processing code required. Specifically, for the text-to-SVG task, the optimization-baser method SVGDreamer excels in enhancing editability by employing a semantic-driven image vectorization process that effectively separates foreground objects from the background, while failing to handle complex scenes. Another optimization-basee work, VectorFusion, stands out for generating SVG- exportable vector graphics without relying on large captioned datasets. However, Vectorfusion is also unable to handle complex scenarios and diverse styles. The significant problem with these optimization-base works is that the optimization time is too long. Generating SVG usually takes more than ten minutes, which is too expensive. For the LLLM-based method, Char2SVG integrates Large Language Models (LLMs) with image diffusion models create semantically rich SVG templates. However, Chat2SVG still needs to optimize the output SVG script from LLM, which introduces increased computational complexity and poses challenges during model training. In comparison, IconShop utilizes a transformer-basse sequences, demonstrating exceptional performance in simplified icon SVGs, which offers effective solutions for text-to-SVG generation. It can only generate black simple Icon SVGs.
architecture to autoregressively model SVG path
#Tokans: 31k
#Tokens: 14k
#Tokens: Ok
#Tokens: 2k PPathe: 23
#Pethe: 351
#Pethe: 121
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#Takans: 28k
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#Takens: Blk #Paths: 61
#Tekens: 2k #Paths: 21
#Potha: 297
#Paths: 14
Reference
Scale 1
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Figure 9: Image Prompting Dataset Creation of MMSVG-2M Character. By utilizing FLUX- and SVG vectorization tools, image prompting data pairs can be generated. We adpot FLUX-Redus downsampling scale with 2, 3 in practice by trading-off off the character similarity and complexity of generated SVG.
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10
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(a) Training PPL for our models.
(b) Validation PPL. for our models.
Figure 11: Training and Validation Perplexity (PPL) for OmniSVG Models. We train all the models from scratch on 250 billion tokens. observe that performance grows with model sizes.
For the image-to-SVG task, we compare our method with the image vectorization methods. LIVE allows progressive and efficient generation of SVGs, optimizing closed vector paths under raster image supervision with shape complexity control. However, LIVE needs to optimize for a long time when generating complex SVGs. DiffVG enables end-to-end differentiability in vector graphics rasterization, improving optimization through anti-aliasing and gradient- based methods while also computationally expensive due to the complexity of the forward- forward-backward backward rasterization process. Recently, the Multimodal Large Language Model (MLLM) based method Star Vector leverages the visual understanding apply accurate SVG primitive to the LLM architecture, which also can generate SVGs from both text and image inputs. However, it still fails to generate complex SVGs. Since Starvector [22 has not yet opened up its text-to-SVG model weights, MMSVGBeench does not evaluate Starvector's text-to-SVG capabilities. MMSVG-Benne also evaluates our methods with VLM methods, GPT-4o, to conduct comprehensive assessment. We compare our method with these baselines on our MMSVG-2M dataset, from simple MMSVG-BE datset, bit complex MMSVG-illustraioon dataset, the very complex MMSVG-Character dataset.
D More details of the baselines
D.1 Text-to-SVG Task
SVGDreamer (60 uses semantic-driven image vectorization (SIVE) process to separate foreground objects and background, improving editability. The SIVE process utilizes attention-based primitive control and an attention- mask loss function to manipulate individual elements effectively. To address issues in existing text-to-SVG generation methods, the proposed Vectorized Particle-based Score Distillation (VPSD) approach models SVGs as distributions of control points and colors, improving shape, color diversity, and convergence speed.
VectorFusion 22 leverages a text-conditioned diffusion model trained on pixel representations to generate SVG exportable vector graphics without needing large captioned SVG datasets. By optimizing a differentiable vector graphics rasterizer, it distills semantic knowledge from a pretrained diffusion model and uses Score Distillation Sampling generate SVG consistent with caption. Experiments show that VectorFusion improves both quality and fidelity, offering a variety of styles such pixel and sketches.
Chat2SVG (56] proposes hybrid framework that combines the strengths of Large Language Models (LLMs) and image diffusion models for text-to-SVG generation. The approach first uses an LLM to create semantically meaningful SVG templates from basic geometric primitives. A dual-stage optimization pipeline, guided by image diffusion models, refines paths in latent space and adjusts point coordinates to enhance geometric complexity.
uses transformer-basee architecture to encode path commands and learn to model SVG path sequences autoregressively. It has shown excellent results in simplified icon scenarios and provides good solution to TeXt-to-SVG generation by extending the FIGR-S-SVC dataset with
IconShop 57
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Icon
@
A
Illustration
Character
Figure 12: Illustratoon of the SVG Generation Capabilities of OmniSVG.
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captions. We have access to their dataset and original splits and have trained our model on that data using a pre-trained checkpoint (trained on OmniVG dataset). We have extracted the results from IconShop and included them here to compare our method.
LLM4SVG [59] is framework that leverages Large Language Models (LLMs) understand and generate Scalable Vector Graphics (SVGs). It employs structured SVG encoding approach, utilizing learnable semantic tokens accurately represent SVG components and their properties. This design enables LLMs to produce SVGs that are both semantically aligned with textual descriptions and visually coherent. However, LLM4SVG also has maximum token length of 2048, limiting its ability to generate highly complex SVGs that require longer sequences.
D.2 Image-to-SVG Task
LIVE (Layer-wise Image Vectorization) 34 is method for progressively generating SVGs that closely fit a given raster image by recursively adding and optimizing closed vector paths. Using differentiable renderer (based on DinvG 29).. LIVE enables direct optimization of paths under raster image supervision while controlling shape complexity by adjusting the number of path segments. introduces component-wise path initialization, identifying key visual components ensure efficient topology extraction and minimize redundant shapes.
DifTVG 29 is landmark in vector graphics research, pioneering deep learning- -aased methods with the first differentiable vector graphics rasterization pipeline. By leveraging combination of anti- aliasing techniques and gradient-basee optimization, DifVVG ensures differentiability. Unlike methods relying on non-differentiable curve-to-mesh conversions, DiffVG employs a forward-backe rasterization process, where the forward pass generates antialiased images and the backward pass computes gradients with respect to vector graphic parameters.
Veator [42 works directly in the SVG code space, leveraging visual understanding to apply accurate SVG primitives. Star Veator employs a transformer-base image encoder with language model, enabling it to process visual inputs and produce precise SVG code. Star Vector effectively handles diverse SVG types, including icons, logos, and complex diagrams, demonstrating robust generalization across various vectorization tasks. However, with 16k token context window, Star Vector may struggle to process highly complex SVGs that require longer sequences.
architecture that integrates an
Vtracer [12] an image processing algorithm designed to convert raster images into SVGs. The algorithm follows three-step pipeline, which involves the hierarchical clustering of images for vectorization. Initially, the pixels are transformed into paths, which are subsequently simplified into polygons. In the final step, these polygons smoothed and approximated using a Bezier curve fitter.
27


Reviews:
[{"review_id": "pOHXBfLVen", "reviewer": "Reviewer_7FNb", "summary": "This work introduces two components:\n1. MMSVG-2M, a preprocessed dataset of vector graphics. Where the authors provide caption-SVG pairs, illustration-SVG pairs, and character-SVG pairs (generated via VTracer)\n2. The authors introduce a tokenization scheme to facilitate SVG generation, and finetune Qwen 2.5 to perform SVG generation\n\n\nThe authors demonstrate state-of-the-art results on SVG generation.", "questions": "1. In the supplemental, Table 7. `You are a helpful SVG Generation assistant, designed to generate SVG. We provide two image as input, the second image is the character reference image of the first image, generate the character reference SVG based on these two input images.` -- is this prompt incorrect? What are the two images as input? I was under the impression that there was only one image as input in the character generation case.\n\n2. VTracer should really be mentioned more prominently. You utilize this tool for SVG generation of the characters, and this is only mentioned in Figure 2 of the main text.\n\n3. Could the authors describe their tokenizer in more detail? I am particularly interested in how the point coordinates are preprocessed and tokenized. Are the coordinates normalized to image width/height? Are the images assumed to have equal width/height? Are the coordinates quantized before tokenization?\n\n4. Could the authors describe their token output format in more detail? Do the authors utilize some kind of constrained sampling to ensure that the output always follows a specific grammar? \n\n5. Could the authors describe the difference between the icon & illustration subsets of the dataset in terms of how they are used? Are they both used for text-to-SVG or illustration-to-SVG?", "limitations": "1. The reliance on synthetic captions seems to be a large constraint. Due to this, more niche characters or world knowledge may be lost in the SVG generation pipeline.\n2. The character to SVG task is not super meaningful in my view. Since the data is derived synthetically anyways. \n3. The authors do not demonstrate any multi-round image editing. In my view, this would enable to previous character-to-SVG task to be a meaningful task.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "## Strengths:\n\n1. OmniSVG handles complex SVGs up to 30k tokens, surpassing previous methods.\n2. This method supports text, image, and character-reference inputs.\n3. The dataset itself (if made public) MMSVG-2M would be a huge contribution, this would be a super diverse dataset for SVG research.\n4. The method outperforms baselines in quantitative and qualitative evaluations.\n\n## Weaknesses:\n1. Generating complex SVGs can be time-consuming. It isn't clear if this can be further optimized with some kind of multi-resolution implementation (test-time scaling) or some improved tokenizer\n2. Writing generally could be improved. \n\n**Generally I like the paper, if the authors can provide additional clarity around the method I would be happy to take another look.**", "was_revised": true, "final_justification": "This paper in the current form is not well written. This is not to deny the massive contribution of the dataset, and the successful demonstration that a text-to-svg or image-to-svg model could be trained on such a dataset. \n\nHowever the paper as submitted, lacks significant details regarding the implementation of the tokenizer and how the authors accomplish different tasks. I think a good paper should not only demonstrate strong technical achievement, but also do a good job in explaining their work to others.", "rebuttal": "We thank the reviewer for recognizing: 1) the ability to handle complex SVGs, 2) the versatility of our multi-modal framework, 3) the substantial contribution of MMSVG-2M dataset, and 4) the superior performance. \n\nWe thank the review for the valuable feedbacks for us to improve our paper. We will address your concerns and carefully revise the paper accordingly. Please find our point-to-point response to your concerns below.\n\n📝 **W1: Time-consuming promblem of generating complex SVGs.**\n\n💡**A:** The generation efficiency for complex SVGs is a challenging topic for existing methods. However, thanks to the OmniSVG tokenizer, we are able to generate complex SVGs with only 6,000 OmniSVG tokens, while StarVecter would require 30,000 tokens. Since SVG is a resolution invariant visual representation, we are unable to speed up the generation with the multi-resolution implementation. However, modern technologies like multi-token generation [1], linear [2], or even log-linear attention [3] could be a promising way to achieve a much faster generation speed.\n\n[1] Fabian et al. Better & faster large language models via multi-token prediction. arXiv preprint arXiv:2404.19737, 2024.\n\n[2] Katharopoulos et al. Transformers are RNNs: Fast autoregressive transformers with linear attention. In ICML, pages 5156–5165. PMLR, 2020.\n\n[3] Guo et al. Log-linear attention. arXiv preprint arXiv:2506.04761, 2025.\n\n📝 **W2: Writing generally could be improved.**\n\n💡**A:**  Thank you for your valuable feedback. We will improve our writing and address your concerns highlighted in the `question session` with more details in our revision.\n\n📝 **Q1: Clarification on the two-image input for character reference SVG generation.**\n\n💡**A:**   In the character reference SVG generation task, generating SVGs directly from natural images is challenging. To address this, we introduce a two-stage training approach. Stage 1: Initially, we input two images into the model: the first is the original natural image, and the second is a rasterized version of a target SVG, serving as a reference in SVG style. This dual-image setup allows the model to leverage the capabilities of a pretrained Image-to-SVG model while incorporating the more complex natural image of the character as an additional input. This helps the model gradually acquire the ability to generate character-specific SVGs. Stage 2: In the later stage of training, we gradually drop the second (reference) image. This encourages the model to retain its character SVG generation ability while transitioning from an Image-to-SVG paradigm to a more general character-to-SVG generation directly from natural images. This two-stage training strategy provides better control over style and detail compared to direct Image-to-SVG approaches, especially in scenarios requiring simplified vector graphics. We acknowledge that our original explanation was unclear, and we will elaborate further on the differences between these tasks and the advantages of dual-image input in the revised version.\n\n📝 **Q2: In comparison with VTracer.**\n\n💡 **A:** We have also conducted a quantitative comparison with VTracer. Since images cannot be included during the rebuttal period, we report the quantitative comparison results with VTracer. For a fairer comparison, we compared our method with VTracer under two settings: preserving VTracer's performance (with longer tokens) and using the same token count (sacrificing performance). Following our paper,  we calculate the average token length (# Tokens) of a generated SVG sample utilizing the Qwen2.5-VL tokenizer. As shown in the results below, our method achieves a trade-off between generation quality and token count (editability) that VTracer cannot achieve. Given the increased SVG tokens, VTracer achieves better generation quality at the cost of editability. \n\n| Method | #Tokens↓ | DINO↑ | SSIM↑ | LPIPS↓ | MSE↓ | \n|-|-|-|-|-|-|\n| VTracer | 7117 | 0.9833 | **0.985** | **0.0245** | **0.0051** | \n| VTracer(same token level) | 2418 | 0.9416 | 0.912 | 0.0672 | 0.0120 | \n| OmniSVG | 2144 | **0.9865** | 0.937 | 0.0587 | 0.0123 | \n\n📝 **Q3: More details of the SVG tokenizer.**\n\n💡 **A:** Regarding the implementation details of our SVG tokenizer, our method first preprocesses all SVG graphics to a standard 200×200 canvas, ensuring consistent coordinate space across inputs of different sizes. For coordinate quantization, we discretize all coordinate values before tokenization by truncating decimal parts to map floating-point coordinates to the integer domain. In the specific tokenization process, we adopt a coordinate merging strategy, encoding 2D coordinate points into single tokens through the mapping function <x, y> → x × w + y, where w=200 is the unified canvas width. This design reduces SVG sequence length and improves generation efficiency. Additionally, we assign dedicated tokens for six SVG command types {M, L, C, A, Z, F} and use 4096 special tokens to represent color hex values. The entire SVG script is flattened into a single command sequence, where each path begins with a drawing command followed by corresponding coordinate tokens. Finally, the entire SVG sequence is mapped to the same representation space as the pretrained VLM through learnable embedding layers, achieving unified modeling of images and SVG.\n\n📝 **Q4: Details of the token output format.**\n\n💡 **A:** Our token output format strictly follows predefined SVG syntax specifications. Specifically, our tokenizer constrains SVG path representations to 5 fundamental path commands (M, L, C, A, Z) with their corresponding coordinate parameters, as well as the color command F paired with color tokens. This design ensures structured and parsable outputs. During generation, the model's output space is naturally constrained within this predefined token vocabulary, where each token corresponds to specific SVG syntax elements, thereby guaranteeing that generated sequences always conform to valid SVG path syntax. This design not only simplifies the model's learning task and prevents generation of invalid SVG code, but also improves generation efficiency. We demonstrate the application effect of this token format in actual generation processes in detail in the supplementary material 2245_OmniSVG_A_Unified_Scalabl_Supplementary Material/assets/OmniSVG-main-demo-1080.mp4, where it can be observed that each token output by the model strictly adheres to this syntax specification, ensuring the validity and editability of generated results.\n\n📝 **Q5: Clarification on the usage of the icon and illustration subsets.**\n\n💡 **A:** Thank you for your question. Our dataset is composed of three subsets, including icon, illustration, and character, each representing a different level of visual complexity and application scenario. All three subsets are used during training for both text-to-SVG and image-to-SVG tasks. Specifically, the icon subset consists primarily of concise icon designs, typically composed of basic geometric shapes with fewer paths (average token count of ~4k using the Qwen2.5-VL tokenizer), making it well-suited for interface and UI design. The illustration subset includes more complex and diverse SVGs at the illustration level, encompassing a wider range of artistic styles and detailed structures, making it suitable for creative and visual art design tasks.\n\n📝**L1:  Impact of synthetic captions on niche characters and world knowledge in SVG generation.**\n\n💡 **A:** We acknowledge the concern about the potential loss of niche characters and world knowledge in the SVG generation pipeline. For our MMSVG-2M-Character dataset, 50% of the data is from real SVG files collected from the web, preserving diversity and authenticity, while the other 50% is generated using FLUX-based models with vector-style LoRA and vectorized through VTracer. During caption generation, we adopted a generalizable strategy, avoiding over-reliance on proper nouns and instead introducing specific concepts through context to balance accuracy and generalization. However, avoiding world knowledge loss entirely presents challenges. Overemphasizing proper nouns would hurt the model’s generalization, while current captioning techniques still struggle with highly specific or niche knowledge. Despite ensuring data quality through PSNR and SSIM filtering and mixing real and synthetic data, this trade-off remains unavoidable under current technology. We look forward to advancements in semantic understanding and knowledge preservation.\n\n📝**L2: Justification of the significance of the character-to-SVG task despite synthetic data.**\n\n💡**A:** The character to SVG task possesses significant practical value in real world applications. In domains such as graphic design, animation production, and brand identity design, designers frequently need to convert character images into editable vector formats. Our method can automatically accomplish this conversion process, substantially improving workflow efficiency.\n\n📝 **L3: Clarification on the lack of multi-round image editing and its potential impact on the character-to-SVG task.**\n\n💡 **A:** We fully agree that multi-round image editing holds significant importance in practical applications, and this interactive editing approach can indeed substantially enhance the utility and controllability of SVG generation tasks. In this work, we focused our research on the core parametric representation problem in text-to-SVG and image-to-SVG generation, as high-quality parametric representation forms the foundation for achieving precise editing. Although the Qwen2.5-VL model architecture we adopted inherently supports chain-of-thought (CoT) reasoning and multi-round dialogue capabilities, considering that multi-round editing tasks require maintaining lengthy context information and complex state management, given our current limited computational resources, we prioritized ensuring the quality and stability of single-round generation tasks."}, {"review_id": "bzFWcrP6wj", "reviewer": "Reviewer_mQ97", "summary": "The paper proposes a generative model for SVG that supports texts, images, or character reference inputs. The method proposes a parameterization and tokenization for SVG commands and introduces a large-scale SVG dataset for training. Results show better performance than prior methods that support only one of the text-conditioned or image-conditioned tasks.", "questions": "* Does the model support multi-turn generation or free-form text inputs, e.g., some editing instructions for a reference SVG image? In other words, is the instruction following capability of the pre-trained VLM lost after the proposed fine-tuning process?", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 1, "strengths_and_weaknesses": "Strengths\n* The paper proposes a unified condition-to-SVG framework that leverages the strength of pre-trained VLMs. \n* The collected dataset will advance the research in this topic. \n* The paper shows empirical performance gain over prior works.  \n\nWeaknesses\n* The main concern is insufficient evaluation. For image-to-SVG task, the pixel-wise reconstruction performance should be reported. Current evaluation metrics also don't reflect how output SVG preserves input character references due to the coarseness of the perceptual metrics used in this paper. Since the dataset contains ground truth SVG sequences, it's also possible to report metrics compared with GT sequences. \n* The paper proposes an autoregressive framework that may suffer from error propagation. How does the method perform for SVG sequence outputs of different lengths, reflecting different complexities of the task? This aspect should also be quantitatively evaluated. \n* The paper follows a standard VLM fine-tuning pipeline dedicated to SVG generation task. Discussions and insights on how such pipeline can be adapted to other inverse graphics tasks or tasks in other domains (or even small scale experiments) will strengthen the paper.", "was_revised": true, "final_justification": "All my concerns are well addressed in the additional experiments and clarifications in the rebuttal. I found this work to have solid empirical performance superior to prior works and therefore vote for acceptance.", "rebuttal": "We thank the reviewer for approval of: 1) our unified multimodal framework that elegantly handles diverse input conditions for SVG generation, 2) the contribution of a large-scale dataset, and 3) the demonstrated empirical superiority with comprehensive performance improvements over existing single-modality approaches.  \n\nPlease find our point-to-point response to your concerns below.\n\n📝 **W1: More quantitive evaluations.**\n\n💡 **A:**  Regarding pixel-wise reconstruction metrics, following StarVector, we have reported DINO, SSIM, LPIPS, MSE in the manuscript. Additionally, we also provide PSNR metrics as shown below. We appreciate your suggestion and have also calculated edit distance metrics between the output SVG and ground truth sequences using the method from [1]. To facilitate comparison, we report the normalized edit distance, which is calculated as $  \\frac{\\text{edit distance}(\\text{generated}, \\text{gt})}{\\max(\\text{len(generated)}, \\text{len(gt)})}  $, where a smaller value indicates greater similarity between the generated SVG sequence and the ground truth (GT) sequence.  The following table shows that the normalized edit distance of the OmniSVG method is significantly smaller than that of the baseline, suggesting that the SVG sequences generated by our method are much closer to the ground truth (GT) sequences.\n\n \n| Method    | DINO↑  | SSIM↑  | LPIPS↓ | MSE↓  | PSNR↑ | edit distance (normalized) ↓ |\n|-----------|--------|--------|--------|-------|-------|---------------|\n| LIVE| 0.960  | **0.979**  | **0.034**  | **0.004** | **26.12** | 0.953          |\n| DiffVG | 0.951  | 0.956  | 0.056  | 0.015 | 20.52 | 0.958          |\n| StarVector| 0.977  | 0.944  | 0.044  | 0.012 | 21.82 | 0.840          |\n| OmniSVG   | **0.980**  | 0.954  | 0.049  | 0.011 | 22.78 | **0.712**          |\n\n\n[1] https://github.com/roy-ht/editdistance\n\n📝 **W2: Clarification of the potential error accumulation.**\n\n💡 **A:** We fully acknowledge the potential error accumulation issue inherent in autoregressive frameworks and have conducted systematic quantitative evaluations on SVG sequences of varying lengths. We divided the test set into four ranges based on token length (0-256, 256-512, 512-1024, 1024-2048) and comprehensively evaluated the performance using six metrics: DINO similarity, MSE, PSNR, SSIM, LPIPS, and text similarity, comparing OmniSVG with the current state-of-the-art baseline StarVector. Note that we calculate the average token length (# Tokens) of a generated SVG sample utilizing the Qwen2.5-VL tokenizer.\n\nThe experimental results show that as the sequence length increases, all metrics decline, with DINO similarity gradually decreasing. However, this degradation is relatively gradual. Compared to the baseline method, StarVector, which also uses an autoregressive architecture, OmniSVG performs better across all sequence lengths and metrics. For the longest sequences, OmniSVG achieves significantly higher performance in both DINO similarity and text similarity, demonstrating its superior capability in generating high-quality outputs. Thanks to our parameterization design and simplified representation, error propagation in the autoregressive model is not significantly noticeable in our approach.\n\nThese results indicate that although the inherent error propagation characteristics of autoregressive frameworks are unavoidable, our method effectively mitigates quality degradation in long sequence generation through stronger representation learning and generation capabilities, demonstrating better robustness when handling complex SVG tasks.\n\n| #Tokens| Method | DINO↑ | MSE↓ | PSNR↑  | SSIM↑  | LPIPS↓ | \n|-|-|-|-|-|-|-|\n| 0-256 | OmniSVG | **0.985** | **0.005** | **25.45** | **0.977** | **0.021** |\n| | Starvector | 0.979 | 0.007 | 24.36 | 0.964 | 0.029 | \n| 256-512 | OmniSVG | 0.980 | 0.008 | 23.96 | 0.961 | 0.037 | \n|  | Starvector | 0.971 | 0.011 | 23.01 | 0.958 | 0.044 |\n| 512-1024 | OmniSVG | 0.977 | 0.012 | 22.73 | 0.953 | 0.049 | \n|  | Starvector | 0.968 | 0.014 | 22.09 | 0.947 | 0.052 | \n| 1024-2048 | OmniSVG | 0.968 | 0.013 | 22.02 | 0.946 | 0.053 | \n| | Starvector | 0.962 | 0.021 | 19.11 | 0.939 | 0.059 | \n\n📝 **W2: More discussion on the VLM fine-tuning pipeline.**\n\n💡 **A:** The OmniSVG approach based on pre-trained VLMs can indeed be adapted to other inverse graphics tasks, particularly demonstrating significant application value in unstructured graphical representation domains such as CAD and 3D mesh generation. In CAD generation, CAD-MLLM [2] and CAD-GPT [3] have demonstrated the feasibility of utilizing multimodal large language models to generate CAD command sequences, where CAD-MLLM supports diverse inputs including text, images, and point clouds, while CAD-GPT enhances spatial reasoning capabilities to achieve precise synthesis from single-view images or text. In 3D mesh generation, AutoSDF [4] and MeshGPT [5] learn discrete tokens and reconstruct 3D representations using VQVAE models, PolyGen [6] employs two decoder Transformers to sequentially predict vertex positions and connectivity, and MeshXL [7] explores explicit sequence modeling approaches for high-fidelity 3D mesh generation. The core advantage of these methods lies in fully leveraging the powerful representation capabilities of pre-trained VLMs, enabling easy extension to these tasks. As shown in Table 5, competitive performance can be achieved through simple task-specific fine-tuning, while acceleration strategies for VLMs make processing complex graphical representations feasible. More importantly, the successful deployment of industrial systems such as Hunyuan3D-PolyGen [8] demonstrates the scalability and practicality of pre-trained model-based approaches in real-world applications. This unified framework not only avoids the high cost of training specialized models from scratch for each inverse graphics task, but also facilitates knowledge transfer between different graphical representations through shared visual-language understanding capabilities.\n\n[2] Xu J, Zhao Z, Wang C, et al. Cad-mllm: Unifying multimodality-conditioned cad generation with mllm. arXiv preprint arXiv:2411.04954, 2024.\n\n[3] Wang S, Chen C, Le X, et al. Cad-gpt: Synthesizing cad construction sequence with spatial reasoning-enhanced multimodal llms. AAAI Conf Artif Intell, 2025, 39(8): 7880-7888.\n\n[4] Mittal P, Cheng Y C, Singh M, et al. Autosdf: Shape priors for 3d completion, reconstruction and generation. IEEE/CVF CVPR, 2022: 306-315.\n\n[5] Siddiqui Y, Alliegro A, Artemov A, et al. Meshgpt: Generating triangle meshes with decoder-only transformers. IEEE/CVF CVPR, 2024: 19615-19625.\n\n[6] Nash C, Ganin Y, Eslami S M A, et al. Polygen: An autoregressive generative model of 3d meshes. ICML, PMLR, 2020: 7220-7229.\n\n[7] Chen S, Chen X, Pang A, et al. Meshxl: Neural coordinate field for generative 3d foundation models. NeurIPS, 2024, 37: 97141-97166.\n\n[8] Weng H, Zhao Z, Lei B, et al. Scaling mesh generation via compressive tokenization[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 11093-11103.\n\n📝 **Q1: The capability of the pre-trained VLM would not lost after the proposed fine-tuning process.**\n\n💡 **A:** Since our training supervision focuses exclusively on SVG generation, the current OmniSVG model weights cannot directly perform VLM-related tasks such as QA, Captioning, or Grounding. However, OmniSVG demonstrates strong instruction following capabilities in text-to-SVG and image-to-SVG tasks, generating SVG tokens that comply with the given language or image conditions. \n\nImportantly, our model architecture preserves the core architecture of Qwen2.5-VL and only extends it through vocabulary expansion during training. Therefore, it can seamlessly support co-training of SVG generation tasks with VLM tasks to retain the corresponding VLM capabilities. Similarly, other research utilizing VLMs for various tasks (such as RT-2 [9]) has shown that this co-training approach can enhance model generalization on high-level instructions and even improve instruction following capabilities through chain-of-thought reasoning and multi-turn dialogue.\n\nAs this paper focuses on SVG generation subtasks to enable better comparison with previous work, we currently concentrate on these specific tasks. We believe that this co-training paradigm represents a highly valuable direction for future exploration, and we will continue to investigate this approach.\n\n[9] Zitkovich B, Yu T, Xu S, et al. Rt-2: Vision-language-action models transfer web knowledge to robotic control[C]//Conference on Robot Learning. PMLR, 2023: 2165-2183."}, {"review_id": "kYlraER2xU", "reviewer": "Reviewer_zqpQ", "summary": "The paper proposes OmniSVG, a framework for autoregressive SVG generation by leveraging pre-trained VLMs.\n\nSVGs that are composed of multiple paths, curves, and decomposed into layers are tokenized into sequences where type identifier are represented by characters, continuous coordinates are discretized, and special tokens added to indicated the start and end of svg sequence. These tokens are then projected to the same embedding space of the pre-trained VLM through a learnable embedding layer. Text or image conditions are tokenized with the frozen VLM tokenizer, and used as prefix tokens.\n\nThe pre-trained VLM is finetuned to produce SVG tokens autoregressively.\n\nThe authors introduce a large annotated SVG dataset containing 2 million samples, divided into three subsets based on SVG complexity, with three benchmark tasks. Comprehensive quantitative evaluations and qualitative comparisons on each task demonstrate that the proposed method outperforms prior optimization-based, auto-regressive, and diffusion-based methods in both quality and efficiency.", "questions": "- Is the reason why the number of tokens used in MMSVG-Character is x3 times more than MMSVG-Illustration because the dataset for the former contains less artist curated SVGs and more VTracer SVGs, where redundant and over-parameterized curves can be present? The training samples shown in Fig 2 do not explain why more tokens are needed for characters as they appear to have the same (visual) complexity as samples from illustrations.\n\n- Is the embedding layer just a linear layer? Details about its implementation is missing in the paper.\n\n- Example questions used in the user study and task given to the participants should be shown in the paper (or supplemental).", "limitations": "The authors have adequately addressed the limitations, namely (1) the wall-clock time during inference, and (2) performance on out-of-distribution raster images for the image-to-SVG task.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n\n**(+1)** The authors propose a simple yet effective technique to re-purpose VLMs for SVG generation. Ablations show that their choice of SVG parameterization is important for compatibility with pre-trained VLMs.\n\n**(+2)** Evaluation of the system is thorough. Qualitative results in the main paper and supplemental demonstrate that the next-token prediction framework is capable of modeling complex SVG shapes.\n\n**(+3)** The quantitative metrics are equally comprehensive.\n\n**(+4)** The authors contribute a valuable dataset.\n\n**Weakness**:\n\n**(-1)** While the evaluation methods demonstrate OmniSVG’s improved performance over other learning-based methods, it is less conclusive when compared with optimization-based methods. Optimization-based methods that minimize a reconstruction objective with geometric regularizers like DiffVG and LIVE are able to produce shapes as complex as OmniSVG for the task of image-to-SVG in comparable time. Qualitative and quantitative evaluations corroborate this. Optimization-based methods have the advantage of requiring no training data. While OmniSVG uses fewer tokens, evaluations should be done to show that OmniSVG produces SVG that model the distribution of curves and paths and layer decomposition desired by users, attributes that optimization-based methods cannot capture. This evaluation can be done qualitatively, where the control points and handles of the primitives and parametric curves can be shown, including their layer decomposition.\n\n**(-2)** DiffVG and LIVE can be combined with a text-to-image model, making it a strong baseline for text-to-SVG. Currently, this experiment is missing.\n\n**(-3)** The SVG tokenization is heavily borrowed from IconShop, with the additional token for color fill (a trivial introduction). Fig 6 (ablation) shows that this choice of parameterization is crucial, despite using the same VLM under-the-hood. To make a fair comparison to IconShop and justify the fine-tuning of a VLM and overall contribution of the paper, training an auto-regressive transformer of comparable size from scratch (like in IconShop) with the additional fill color token on the proposed MMSVG-2M dataset is an important baseline. Alternatively, justifying the importance of the VLM, like its generalization capabilities, is needed.\n\n**(-4)** Failure case shown in Fig 8 is one most image-to-vector methods will suffer from as the input image contains signals that standard vector primitives with solid fill attribute cannot model. Instead, it would be more meaningful to test image-to-SVG on vector shapes with complex geometry – more complex than characters – and containing complex appearance like linear or freeform gradients, ones that can be modelled in common design software. Text-to-image models can provide good candidate images.\n\n**(-5)** I don’t agree with the statement that OmniSVG decouples structural logic from low-level geometry. The tokenized SVG, like the original XML file, is imperative and contains the same information – curve type, coordinate. However, the chosen parameterization is more compact and I suspect this might be the reason why it is more suitable for VLMs in practice.", "was_revised": true, "final_justification": "The authors have resolved all my concerns and I am satisfied with the rebuttal.\n\nAfter reading all the other reviews and their respective rebuttal, my final rating will be accept.", "rebuttal": "We sincerely thank the reviewer for recognizing our key contributions: 1) an innovative approach adapting pre-trained VLMs for SVG generation, 2) comprehensive experimental validation, 3) a substantial dataset of 2 million annotated SVG samples, and 4) ablation studies validating our SVG parameterization choices.\n\nPlease find our point-to-point response to your concerns below.\n\n\n📝 **W1: More analyses about the comparisons with DiffVG and LIVE.**\n\n💡 **A:** We provide comprehensive quantitative comparisons with optimization methods including LIVE and DiffVG in Table 3 of our paper and Table 8 in the supplementary material. For convenience, we report the the results corresponding to the papers OmniSVG, LIVE, and DiffVG in the following table. While LIVE achieves better scores on pixel-level reconstruction metrics (SSIM, LPIPS, MSE), OmniSVG significantly outperforms LIVE on DINO semantic similarity , indicating that our method better captures high-level semantic features of input images.\n\n| Dataset | Methods | DINO↑ | SSIM↑ | LPIPS↓ | MSE↓ |#Tokens |\n|-|-|-|-|-|-|-|\n| MMSVG-Icon | LIVE | 0.960 | **0.979** | **0.034** | **0.004** | 18.2k |\n| | DiffVG | 0.951 | 0.956 | 0.056 | 0.015 | 19.8k |\n| | OmniSVG | **0.980** | 0.954 | 0.049 | 0.011 |3.8k |\n| MMSVG-Illustration | LIVE | 0.959 | **0.960** | **0.044** | **0.011** |18.2k |\n| | DiffVG | 0.929 | 0.930 | 0.077 | 0.021 |19.8k |\n| | OmniSVG | **0.974** | 0.944 | 0.069 | 0.019 |9.7k |\n| MMSVG-Character | LIVE | 0.911 | **0.939** | 0.109 | 0.038 |18.2k |\n| | DiffVG | 0.876 | 0.915 | 0.163 | 0.051 |19.8k |\n| | OmniSVG | **0.921** | 0.917 | **0.049** | **0.021** | 30.8k|\n\nOmniSVG generates SVGs with much fewer tokens than optimization-based methods (LIVE, DiffVG), reflecting more concise and well-structured SVG generation. This conciseness benefits SVG users by enabling easier understanding and manipulation of control points, Bézier curves, and layer structures. In contrast, while optimization-based methods achieve high pixel-level accuracy, they often produce redundant and irregular path structures that, though visually similar, are harder for designers to comprehend and edit.\n\nOur method learns from real SVG data distributions, generating vector graphics that align with human creation patterns, a key advantage that pure optimization methods cannot capture. Due to rebuttal constraints preventing image displays, we will visualize in the revised version how OmniSVG generates more concise and editable SVG XML files for the same input images.\n\n📝 **W2: Text-to-SVG task comparisons with DiffVG and LIVE.**\n\n💡 **A:** We appreciate the reviewer's suggestion to combine DiffVG and LIVE with text-to-image models as text-to-SVG baselines. In response, we conducted experiments using GPT-4o to convert text prompts into images, which were then processed using DiffVG and LIVE for image-to-SVG tasks, establishing text-to-SVG baselines for comparison. Our results on the MMSVG-Icon test set show that OmniSVG outperforms the baseline methods across multiple metrics, including FID and Aesthetic scores, and also performs well on the HPS metric. While the CLIP scores are similar, OmniSVG achieves this with significantly fewer tokens, demonstrating greater efficiency in generating high-quality SVGs.\n| Method | FID↓ | CLIP↑ | Aesthetic↑ | HPS↑ | #Tokens |\n|-|-|-|-|-|-|\n| LIVE | 88.83 | **0.3322** | 4.88 | 0.234 | 18.3k |\n| DiffVG | 93.23 | 0.3209 | 4.31 | 0.201 | 19.8k |\n| OmniSVG | **64.83** | 0.3194 | **5.49** | **0.247** | 3.8k |\n\nThese quantitative results comprehensively validate the advantages of OmniSVG over existing methods combined with text-to-image models for text-to-SVG tasks. We will supplement the revised version with corresponding qualitative results.\n\n📝 **W3: Fairly comparisons with IconShop and justification the fine-tuning of the VLM.**\n\n💡 **A:** We appreciate the reviewer's suggestion. We extended the IconShop baseline by adding color fill and trained it using IconShop's transformer framework. To validate the importance of VLM, we trained models from scratch by randomly initializing the weights instead of using pretrained VLM weights. Since Qwen2.5-VL-3B-Instruct contains 4B parameters, for fair comparison, we also trained a model from scratch with the same parameter count as IconShop. This comparison primarily evaluates performance differences across different architectures. The results show that the 4B model from scratch performs significantly worse than smaller models, indicating that larger models are more challenging to train, while pretrained models like Qwen provide effective initialization.\n\n| Method | FID↓ | CLIP↑ | Aesthetic↑ | HPS↑ |\n|-|-|-|-|-|\n| IconShop(+fill) | 77.65 | 0.2810 | 4.47 | 0.235 |\n| OmniSVG(from_scratch, 4B) | 84.12 | 0.2727 | 4.18 | 0.228 |\n| OmniSVG(from_scratch, tiny) | 74.29 | 0.2931 | 4.68 | 0.242 |\n| OmniSVG | **64.83** | **0.3194** | **5.49** | **0.247** |\n\nThe results demonstrate that leveraging pretrained VLM significantly improves generation quality across all metrics, validating the importance of VLM's generalization capabilities for SVG generation tasks.\n\n📝 **W4: Failure cases analyses.**\n\n💡 **A:** Thank you for highlighting this limitation. We agree that complex appearance attributes, such as gradients, present significant challenges for current methods. As we cannot upload images during the rebuttal period, we will include additional failure cases in the revised version, specifically demonstrating where our method struggles with SVGs containing linear and freeform gradients. The limitation arises from the inherent complexity of modeling gradients in vector graphics, which requires handling multiple parameters like color stops, interpolation methods, and gradient directions. Our current tokenizer focuses on geometric shapes and solid color fills, and has not yet fully addressed these complex attributes. This is a common bottleneck in deep learning-based SVG generation methods, as the high-dimensional and nonlinear nature of gradient parameters makes end-to-end training difficult. Despite this, our method performs excellently with vector graphics that involve complex shapes and solid color fills, generating highly detailed SVG outputs. This forms a strong foundation for future extensions to handle more complex appearance modeling.\n\n📝 **W5: Clarification of the statement.**\n\n💡 **A:** Thank you for your insightful observation. We agree that the statement \"OmniSVG decouples structural logic from low-level geometry\" is inaccurate and will remove it in the revised version. The core contribution of the OmniSVG tokenizer is to establish a unique and canonical representation for SVG graphics. As the reviewer correctly points out, the tokenized SVG retains the original structure and geometric information but achieves uniqueness through a systematic parameterization scheme. This is essential for VLMs, as multiple representations of the same graphic create ambiguity during learning. For example, a rounded rectangle can be represented either as `<rect width=\"200\" height=\"100\" x=\"10\" y=\"10\" rx=\"20\" ry=\"20\" fill=\"blue\" />` or as a path command. Such diversity makes it hard for models to learn essential features. OmniSVG eliminates this ambiguity through standardized tokenization, ensuring identical visual elements always correspond to the same token sequence, enhancing the efficiency and accuracy of VLMs in understanding and generating SVG.\n\n📝 **Q1: Clarification of the tokens of MMSVG dataset.**\n\n💡 **A:** The 3x increase in token usage for MMSVG-Character compared to MMSVG-Illustration is due to differences in image complexity, not just redundancy from VTracer conversion. Although we removed complex SVG structures using the picosvg tool during preprocessing, character images inherently require more Bézier curves to capture fine details like facial features, clothing textures, and hair, which contrasts with the simpler illustrations featuring large color blocks and concise lines. While Figure 2 shows similar visual complexity, Figure 9 in the supplementary materials highlights that the Character dataset has a higher overall complexity, with more high-complexity images than the Illustration dataset. Additionally, VTracer generates denser path points for character images, which is necessary for accurate reconstruction. The average token count reflects these inherent differences in SVG complexity between the two datasets.\n\n📝 **Q2: Details of the embedding layer.**\n\n💡 **A:** Yes, the embedding layer is a linear layer. The implementation is as follows:\n\n```python\ntorch.nn.Embedding(vocab_size, embed_size)\n```\nFor OmniSVG(4B), the vocab_size is 196,042 and embed_size is 2,048.\nFor OmniSVG-L(8B), the vocab_size is 200,128 and embed_size is 3,584.\n\n📝 **Q3: Details of user study.**\n\n💡 **A:**  We will include the complete user study questionnaire in the supplementary materials of the revised version. Due to space limitations, detailed scoring criteria will be provided in the revised version. Below are some example questions.\n\n**Instructions:** Participants evaluated SVG outputs from 5 methods based on three criteria: Preference (overall quality), Vividness (visual appeal), and Alignment (consistency with input), rating each on a scale of 0 to 100 (0 = worst, 100 = best).\n\n**Part 1: Text-to-SVG Evaluation**\n\n**Sample Format:** Text Prompt: \"A cute cartoon cat sitting on a chair\"\n\nGenerated SVGs from Methods A through E are presented. Participants rate each method using the following table:\n\n| Method | Preference (0-100) | Vividness (0-100) | Alignment (0-100) |\n|-|-|-|-|\n| Method A ||||\n| Method B ||||\n| Method C||||\n| Method D||||\n| Method E||||\n\n**Part 2: Image-to-SVG Evaluation**\n\n**Sample Format:** A source raster image is provided, followed by SVG outputs from Methods A through E. Participants use the same rating table structure as in Part 1 to evaluate each method's performance."}, {"review_id": "GJClPvOiQz", "reviewer": "Reviewer_C11o", "summary": "This paper introduces OmniSVG, a novel framework for generating high-quality Scalable Vector Graphics (SVGs) from multimodal inputs (text, images, or character references). It leverages a pre-trained Vision-Language Model (VLM) to tokenize SVG commands and coordinates, enabling efficient generation of complex, editable SVGs. Key contributions include: 1) OmniSVG: An autoregressive model that decouples structural logic from geometry, addressing \"coordinate hallucination\" in prior methods. 2) ​MMSVG-2M: A large-scale dataset with 2M annotated SVGs (icons, illustrations, anime characters) and a benchmark (MMSVG-Bench) for evaluation.", "questions": "Does the SVG tokenization scheme limit expressiveness by reducing all paths to five basic commands (M, L, C, A, Z)?​​\n\nWhile this simplification standardizes training (Sec. 3.1), it may discard nuanced XML attributes (e.g., transform, stroke-dasharray) and composite operations (e.g., groups) found in professional designs. Two concerns arise:\n\n​Approximation Errors: Complex curves/shapes requiring advanced commands (e.g., quadratic Béziers, filters) must be approximated with cubic Béziers (C). Does this introduce visible artifacts or bloated path data?\n​Editability Trade-off: Simplified paths may ease generation but hinder post-generation editing (e.g., modifying a single element in a grouped shape). How does OmniSVG balance automation with downstream usability?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths​\n1. ​Multimodal Capability: Handles diverse inputs (text, images, references) and tasks (Text-to-SVG, Image-to-SVG, Character-Reference SVG).\n2. ​Dataset & Benchmark: MMSVG-2M fills a gap in complex SVG datasets, and MMSVG-Bench provides standardized evaluation metrics.\n3. SVG tokenization reduces redundancy and improves efficiency.\n4. Superior quantitative metrics (lower FID, higher CLIP/DINO scores) and qualitative outputs (rich colors, geometric accuracy).\n\n\nWeaknesses\n1. ​Limited Evaluation of Complex Characters: While the model performs well on simple icons and animated characters, the paper lacks qualitative results for more intricate designs—particularly detailed characters such as those in ​MMSVG-2M-Character. To fully assess generalization, these examples should be in-the-wild (i.e., not part of the training dataset).\n2. ​Insufficient Showcase of SVG Scalability: One of SVG’s key advantages is its ability to scale without aliasing. However, the authors do not provide enough zoomed-in comparisons to demonstrate this benefit. Including such results would better highlight the model’s precision in preserving geometric fidelity at higher resolutions.", "was_revised": true, "final_justification": "In the rebuttal, the authors provided additional clarification regarding their SVG tokenization, which—while not perfect—is sufficient for most SVG images. Unfortunately, NeurIPS does not permit additional images in the rebuttal, so I could not evaluate results on complex characters or SVG scalability. Nevertheless, given the practical value of this work and its usefulness to the design industry, I believe the paper is acceptable. Therefore, I maintain my score as ​Accept.", "rebuttal": "We thank the reviewer for their approval of: 1) a unified multimodal framework that handles diverse inputs for SVG generation tasks, 2) the MMSVG-2M dataset and MMSVG-Bench, which address the scarcity of complex SVG resources and establish standardized evaluation protocols, 3) an innovative SVG tokenization approach that improves efficiency and reduces redundancy, and 4) demonstrated superiority in both quantitative metrics (FID, CLIP/DINO scores) and qualitative aspects (color richness, geometric precision).\n\nPlease find our point-to-point response to your concerns below.\n\n📝 **W1: Clarification of the evaluation of complex characters.**\n\n💡 **A:**  Before training, we partitioned the data into training, validation, and test sets, ensuring that all test cases were unseen during training. Benchmark results are presented in Figures 4 in the manuscript, with additional test results in Figure 12.  To better demonstrate OmniSVG's generalization capability on complex character designs, we will include qualitative results on real-world character examples outside our training distribution in the revised version. This will provide a more comprehensive evaluation of our model's performance on intricate character designs, as suggested by the reviewer.\n\n📝 **W2: Insufficient Showcase of SVG Scalability.**\n\n💡 **A:** We appreciate the reviewer's valuable suggestion.  In the revised version, we will include zoomed-in view results to further highlight the advantages of our SVG approach. Additionally, we have provided a demonstration of the layer-wise SVG generation process in the supplementary material (2245_OmniSVG_A_Unified_Scalable_Supplementary_Material/assets/OmniSVG-demo-gen-proc-anime-1080.mp4). Furthermore, we will present visualizations of different layers to offer a clearer comparison between our method and optimization-based approaches.\n\n\n📝 **Q1: Clarification of the SVG Tokenizer.**\n\n💡 **A:** Our SVG tokenization consists of five basic path commands (M, L, C, A, Z) along with the color command F. The combination of these six commands is sufficient to represent the majority of vector graphics in practical applications. As shown in Figure 1, even complex characters can be fully represented with just these six commands, demonstrating the expressive power of our selected command set. However, we acknowledge that this approach does not support advanced SVG features such as gradient fills, filter effects, or animation properties. However, by focusing on the most fundamental geometric representation commands, we are able to simplify the tokenization process significantly while retaining enough expressiveness to enable the model to efficiently learn and generate the core structure of vector graphics.\n\n\n📝 **Q2: Clarification of the error accumulation and editability trade-off.**\n\n💡 **A:** Our SVG tokenization approach follows the design principles of DeepSVG, simplifying complex SVG commands into five basic path commands (M, L, C, A, Z) and adding a fill command. This design choice is based on thorough technical considerations and practical effectiveness validation. Regarding concerns about approximation errors, extensive experiments have shown that while there is a theoretical approximation process when converting complex curves (such as quadratic Bézier curves) to cubic Bézier curves, this conversion does not produce visible visual artifacts in practice. Complex character SVG in Figure 1 of the paper clearly demonstrate that OmniSVG can generate high-quality SVG graphics with complex geometric shapes and fine details, proving the effectiveness of our tokenization scheme in maintaining visual quality.\n\nMore importantly, this simplification brings key technical advantages: the generated path representations are unique, eliminating ambiguity caused by multiple equivalent representations of the same shape. This enhances the stability of model training and consistency in the generated results. Regarding the trade-off in editability, we view this as a design choice rather than a limitation. Although the simplified paths cannot be parameterized for editing as whole entities like the original advanced SVG elements, this decomposition actually provides finer-grained control. For instance, by breaking a circle into Bézier curves, users can precisely transform it into an ellipse or other irregular shapes by adjusting control points. This flexibility is often more valuable in professional design scenarios, as it allows designers to make more precise and creative shape adjustments without the constraints of predefined geometric primitives."}]

Author rebuttal:

Meta-review: This work presents a large-scale dataset for vector graphics and proposes a unified framework for generating SVGs under multiple conditions. The authors also introduce an SVG tokenization method that reduces redundancy and improves efficiency. They report strong performance gains in both quantitative and qualitative evaluations.

Reviewers agree on the following strengths:
- The proposed dataset is valuable for future research.
- The approach supports multiple conditions, including text, image, and character-reference inputs.
- Strong empirical performance.

Some concerns regarding limited evaluation were raised, but the authors provided additional results that addressed these issues. One notable weakness is the writing quality. The authors need to improve clarity not only in the text but also in the presentation of images, tables, and experimental settings.

Considering all the points above, the AC recommends a borderline accept.
Decision: Accept (poster)