Title: Bézier Splatting for Fast and Differentiable Vector Graphics Rendering
Venue: NeurIPS 2025
Accepted: yes
Keywords: differentiable vector graphics

Gaussian splatting
Abstract: Differentiable vector graphics (VGs) are widely used in image vectorization and vector synthesis, while existing representations are costly to optimize and struggle to achieve high-quality rendering results for high-resolution images. This work introduces a new differentiable VG representation, dubbed Bézier Splatting, that enables fast yet high-fidelity VG rasterization. Bézier Splatting samples 2D Gaussians along Bézier curves, which naturally provide positional gradients at object boundaries. Thanks to the efficient splatting-based differentiable rasterizer, Bézier Splatting achieves 30× and 150× faster per forward and backward rasterization step for open curves compared to DiffVG. Additionally, we introduce an adaptive pruning and densification strategy that dynamically adjusts the spatial distribution of curves to escape local minima, further improving VG quality. Furthermore, our new VG representation supports conversion to standard XML-based SVG format, enhancing interoperability with existing VG tools and pipelines. Experimental results show that Bézier Splatting significantly outperforms existing methods with better visual fidelity and significant optimization speedup.


Peer reviews:
comments: SUMMARY:
This work presents Bezier Splatting, a new differentiable Vector Graph representation that optimizes Bezier curves through Gaussian splatting-based rasterization. This method provide direct positional gradients at object boundaries so that over 150x faster than DiffVG. Overall, given a raster image, the author efficiently vectorize the input into a VG representation that closely resembles the input while preserving the details.

STRENGTHS AND WEAKNESSES:
Strength: 
1. Vector graph differentiable rendering that make it available for machine learning.
2. Using 2D Gaussian representation, it can provide direct position gradients and it is 150x faster than former State of Art result (DiffVG).
3. Better vectorization results. In both qualitative and quantitative.
4. Vectorization results support flexible editing operations.

Weakness: 
1. Lacking ablation study in Section 4.
2. Lacking experiments on binary images, and comparison with traditional vectorization algorithms.

QUESTIONS:
I wonder if number of curve and other parameters are changeable: 
1. In Line 150, is 10 control points for each curve is fixed or flexible?
2. In table 1, is it necessary to use so many curves? How to determine the best number of curve?
3. Is it possible to make the result more compact? Using fewer curves?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a curve splatting method with 2D Gaussian rendering. Both open and closed curve representations are converted to a set of Gaussians, and those Gaussians are rendered via differentiable rasterization, yielding back-propagation from the image loss to the Bezier curve parameters. Three pruning conditions discard redundant or inefficient curves and replace them with the regions that need more curves.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper’s strengths are as follows;\n\n-The curve-to-Gaussians conversion achieves fast rendering speed compared to the state-of-the-art method.\n- The proposed splatting method achieves the state-of-the-art results for open curves.\n\nThere are the following weak points:\n\n-It would be great to add some figures about open and closed curves with notations.\n\n-A 2D Gaussian splat has a round shape, which poses a problem in representing rectangular shapes. To this end, Gaussian splat representation requires dense sampling as it is a discrete representation.\n\n-The sampling resolution for Gaussians has not been mentioned in this paper. This is crucial as the proposed method aims to reconstruct vector graphics at any image scale. This paper should discuss and examine how dense Gaussian sampling should be with respect to the image resolution.\n\n-Lines 170 and 175 seem duplicated.\n\n-Which probability density function has been integrated into the cumulative distribution function mentioned at Line 170?\n\n-2D grid sampling for the closed curve suffers from non-uniform sampling near the starting and ending points. This increases the rendering time of Gaussians without quality gain. \n\n-A proposed curve pruning method should be explained in more detail. The current explanation looks vague; the overlap threshold at Line 266 and AABB are not mentioned in the “pruning and densification” paragraph. \n\n-Although the rendering time becomes faster, there is no significant improvement in quality.\n\nQUESTIONS:\nI have the following questions and suggestions:\n\n- Does 2D grid Gaussian sampling for closed curves suffer from over-sampling near the starting and ending points? How does it affect the rendering performance, and how could we mitigate this problem? \n\n- Can the current method handle images of various scales without changing sampling parameters? If not, the relation between sampling resolution and image size should be discussed.\n\n- What is the reconstruction policy for curve splatting about depth? \n\n- Please clarify the pruning and densification process.\n\n- For the offline method, the gain in quality is more important than the speed gain. Is there any quality improvement against the state-of-the-art method?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
This work has presented Bézier Splatting, a novel differentiable vector graphics (VGs) representation that leverages Gaussian splatting for efficient Bézier curve optimization. The proposed method achieves faster forward computation and faster backward computation in rasterization compared to the baselines, while also delivering high rendering fidelity. Overall, the idea of this paper is interesting.

STRENGTHS AND WEAKNESSES:
# Strengths
1. The authors propose a novel differentiable vector graphic representation, Bézier Splatting, which achieves 30× faster forward and 150× faster backward computation while producing high-quality rendering results.
2. The authors introduce an adaptive pruning and densification strategy to improve the optimization process of Bézier curves by escaping the local minima of the spatial distributions of curves.
3. Extensive experimental results demonstrate the effectiveness of the proposed method.

# Weaknesses
1. The reviewer wants more explanation about the statement "It helps the optimization process escape the local minima of current spatial distributions of curves". Is there any theoretical analysis that can support this argument?
2. In the experiments, how to determine the hyperparameters \lambda_1 and \lambda_2 in (13)? Is it necessary to change the hyperparameters to fit different datasets?

QUESTIONS:
Please see the weaknesses.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-05