Title: OmniSVG:  A Unified Scalable Vector Graphics Generation Model
Venue: NeurIPS 2025
Accepted: yes
Keywords: SVG Generation

Vision Language Model
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths​\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.\n\nQUESTIONS:\nDoes 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n- 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).\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n* 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.**\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\n1. 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}