ViewCraft3D: High-fidelity and View-Consistent 3D Vector Graphics Synthesis
Authors: Chuang Wang
Haitao Zhou
Ling Luo
Qian Yu
Venue: NeurIPS 2025 poster ()
Keywords: Sketch Generation; 3D Vector Graphic; Diffusion Models
Primary area: applications

Abstract: 3D vector graphics play a crucial role in various applications including 3D shape retrieval, conceptual design, and virtual reality interactions due to their ability to capture essential structural information with minimal representation.
While recent approaches have shown promise in generating 3D vector graphics, they often suffer from lengthy processing times and struggle to maintain view consistency.
To address these limitations, we propose VC3D (**V**iew**C**raft**3D**), an efficient method that leverages 3D priors to generate 3D vector graphics.
Specifically, our approach begins with 3D object analysis, employs a geometric extraction algorithm to fit 3D vector graphics to the underlying structure, and applies view-consistent refinement process to enhance visual quality.
Our comprehensive experiments demonstrate that VC3D outperforms previous methods in both qualitative and quantitative evaluations, while significantly reducing computational overhead. The resulting 3D sketches maintain view consistency and effectively capture the essential characteristics of the original objects.

CrawCraf:3D: High-Fidelity and View-Consistent 3D Vector Graphics Synthesis
Chuang Wang !+ Haitao Zhou Ling Luo 2t Qian Yu
Beihann University
Ningbo University
"Equal contribution
tCorresponding author
We propose ViewCraft3D (VC3D). a method to generate 3D vector graphics from single image. VC3D can leverage 3D prior knowledge to generate high-quality and view-consistent 3D vector graphics.
Figure 1:
Abstract
3D vector graphics play crucial role in various applications including 3D shape retrieval, conceptual design, and virtual reality interactions due to their ability to capture essential structural information with minimal representation. While recent approaches have shown promise in generating 3D vector graphics, they often suffer from lengthy processing times and struggle to maintain view consistency. To address these limitations, that leverages 3D priors to generate 3D vector graphics. Specifically, our approach begins with 3D object analysis, employs geometric extraction algorithm fit 3D vector graphics to the underlying structure, and applies view-cor riew-consistent refinement process enhance visual quality. Our comprehensive experiments demonstrate that VC3D outperforms previous methods in both qualitative and guantitative evaluations, while significantly reducing computational overhead. The resulting 3D sketches maintain view consistency and effectively capture the essential characteristics of the original objects. Project page: github. 1o/VC3D_pagge
propose View Crafi3D (VC3D), an efficient method
Introduction
Three-dimensin dineessiimiio vector graphics offer a unique balance between abstraction and comprehensibility. using minimal line elements convey complex spatial information. These economical representations have become integral to diverse computing applications, from improving immersive experiences in virtual environments facilitating 3D shape retrieval and reconstruction tasks [54] 20 19 [3] (3).
In virtual reality creation environments, 3D vector graphics serve as intuitive building blocks that allow artists materialize spatial concepts directly within immersive spaces [56] [55,, bridging the gap between imagination and digital realization. Recent interactive sketching tools
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39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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have enhanced these creative capabilities by enabling direct manipulation in 3D space. Despite these advances, creating effective 3D vector graphics remains prohibitively difficult for non-specialists due to the intricate combination of spatial reasoning, technical interface skills, and artistic judgment required. This expertise barrier significantly limits widespread adoption and accessibilityy highlighting the need for automated approaches that can generate high-quality 3D vector graphics without requiring users to have specialized training or artistic expertise.
Recent years have witnessed remarkable progress in 2D vector graphics generation. Works like CLI- Passo [36] and CLIPDraw [7] pioneered the use of CLIP's visual-semantic understanding to guide vector graphics optimization. Building on these foundations, methods such as VectorFusion [II) DiffSketcher [45], and SVGDreamer [47] further leveraged diffusion models to achieve higher fi- delity and controllability in vector graphics gen- cration. Concurrently, the field of 3D content cre- 40
37 [5] has been revolution- ized by neural rendering techniques and generative models, making high-quality 3D asset creation in- creasingly accessible. The convergence of these advancements has catalyzed research in 3D vector graphics, with pioneering works like 3Doodle [5] and Diff3DS [58] demonstrating the feasibility of generating expressive 3D line drawings. These approaches have achieved impressive results in creating 3ID vector graphics. However, existing methods predominantly rely 2D generative leveragin models like CLIP (88 and diffusion model (30) as supervision gggalls-wile employing Score Distillation Sampling (SDS) (24] for optimization in 2D projection space rather than directly in 3D. These indirect approaches inherit a fundamental limitation of 2D SDS optimization: cross-view inconsistency, which constrains the ability of methods 24 58 where the same 3D element appears inconsistently from different viewpoints. Even with the use of more powerful pre- trained models, these approaches often struggle to generate coherent 3D vector graphics that remain consistent across arbitrary viewpoints. For example, Diff3DS [88] employs MV 33 to tackle this issue, but the improvement is only partial. On the other hand, 2D priors from pretrained image generation models offer only conceptual-leve guidance, lacking precise recovery of critical lines typically found human-drawn 3D sketches, as illustrated in Figure 2 As a result, the generated outputs often suffer from messy strokes, missing details, and low structural fidelity.
ation 49 59
15
Figure 2: Examples of VR sketches 33
To overcome these challenges, we propose ViewCraft3D (VC3D), novel approach that leverages 3D priors for generating high-fidelity and view-consistent 3D vector graphics. Instead of relying on optimization using 2D priors [58] 5), our method is grounded in 3D geometric attributes within the 3D domain. This allows it to naturally inherit the cross-view consistency of the 3D object while faithfully preserving its spatial structure and geometric details, as illustrated in Figure Specifically, we start by reconstructing a 3D mesh using a pre-trained image-to-3D model. Based on the resulting mesh we identify salient regions in 3D space that capture the object's ''s key structural features. We then perform point-level clustering using spatial proximity and orientation alignment. These clusters are subsequently fitted with 3D Bézier curves, and Chamfer Distance loss is used to ensure accurate geometric approximation. To further refine these vector graphics, we introduce 3D score distillation sampling loss based on pretrained 3D generative models, which optimizes the Bézier curve parameters to enhance both visual quality and structural fidelity. This approach maintains view consistency by construction, as the optimization occurs directly in 3D space guided by 3D priors.
In summary, our contributions are threefold:
We propose ViewCraft3D (VC3D), novel framework for generating high-fidelity 3D vector graphics that leverages 3D priors rather than 2D projections;
We develop two-phase optimization approach combining geometric fitting with 3D-prior guided refinement, significantly improving visual quality.
We conduct extensive experiments demonstrating that our approach outperforms existing methods in both view consistency and generation speed. The results suggest promising directions for future studies.
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2 Related Work
2.1 2D Vector Graphics Generation
Early approaches in 2D SVG generation, such as CLIPasso [6] and CIIPDrra 7. utilized the visual-
semantic understanding of the CLIP model (28] guide vector optimization. Subsequent research
introduced more sophisticated approaches, notably those employing diffusion models [8] 30 6 Work
like VectorFusion (LI), DiffSketcher [45). SVGDreamer [47], and SVGDreamer++ [46] demonstrated
significant improvements in generation quality by employing Score Distillation Sampling [24]. This technique effectively transfers the generative capabilities of pixel-based models to the vector domain. LLM4SVG 43 attempts to use LLM to generate SVG, while Sreason-SVG 411 introduces the idea of reinforcement learning to generate better SVG. In addition, SVGFusion 44 explored the use of the DiT architecture [23] to generate SVG. Furthermore, specialized approaches have been developed for specific applications. These include ford-as-image [T]] for typographic design, CLIPascene (35) for scene sketching with varying abstraction levels, and VectorPainter [9] for stylized graphics synthesis.
More recently, efforts have focused mitigating the computational cost associated with iterative optimization. Works based on autoregressive models, such as Iconshop [39], have demonstrated the potential for rapid generation, significantly reducing processing times. Concurrently, the adaptation of large language models (LLMs) for SVG generation has emerged as another promising research avenue, with works like LLM4SVG employ Vision Vision-Language Language Models (VLMs) as end-to-end multimodal SVG generators. Together, these recent advancements aim ensure high-quality generation while paving the way for future extensions into 3D representations.
42 and Chat2SVG [38]. And OmniSVG (50] attempts to
2.2 Recent Advances in 3D Content Generation
Recent years have witnessed remarkable progress in 3D content generation driven by diffusion- based approaches. Early works like Zero-123 [17] pioneered single-image view synthesis using
geometric priors from diffusion models, while One-2-3-45 [I6] extended this to generate full 360-
degree textured meshes. Multi-view consistency became a focus with MVDream serves as implicit 3D prior through multi-view image generation, and Wonder3D employs cross-domain attention for consistent normal and color generation. Recent innovations have further elevated capabilities: Unique3D [37] improved fidelity through multi-level upscaling, Hun Yuan3D [49 55) achieved photorealistic quality, TripoSG [1] utilized triplane optimization with large-scale data, and Hi3DGen (5]) enhanced geometric fidelity through normal bridging. These cutting-edge approaches primarily focus on generating complete 3D assets with textures and materials, while our work emphasizes the creation of 3D vector graphies that maintain characteristic abstractions and representational efficiency. By leveraging the 3D understanding embedded in these advanced models, particularly TripoSG's s structural representations, we guide our vector optimization toward semantically meaningful and view-consistent results.
132 which
18 which
2.3 3D Vector Graphics Generation
Building upon both the 2D vector graphics techniques and recent 3D generation advances discussed above, 3D vector graphics generation has emerged as a promising research direction. These repre- sentations extend the fundamental advantages of 2D vector graphics while leveraging 3D generative capabilities to model complex spatial structures and depth information. This integration enhances their utility in diverse fields, including web development and digital art. In artistic contexts, works like DreamWire (7 and Fabricable 3D Wire Art (34) have showcased the potential of 3D vector graphics to create compelling, view-dependent visual effects, where the perceived objects change based on viewing angle. To harness these benefits and enable such advanced applications, the development of robust 3D vector graphics generation techniques has become a key research focus. Initial explorations in this area include 3Doodle 5). which pioneered a method for generating 3D vector graphics from multi-view images of the target object. Subsequently, Diff3DS [58] utilized the Score Distillation Sampling to produce 3D vector graphics conditioned on text or image input. Dream3DVG [52) lever-
ages the optimization process of a 3D Gaussian Splatting [12] to establish coarse-to- fine generation approach. However, a notable aspect of these current generative approaches is their predominant reliance on 2D view-specific loss for optimization. View-specific loss is computed independently per camera view, and gradients aggregated across views during optimization. Without strong
3

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Geadurnts Decioven
Pretraized
Emsage
Image
30 Mech M
Peint Closd %
3D 196
Sucapled Point Clard %
LDS
3D Reconstruction
Stage I. Primary Structure Fitting (Sec. 3.2)
Stage II. Detall Refinement (Sec. 3.3)
Figure 3: The overall architecture of the proposed method, showcasing the initial generation of 3D Vector Graphic (3D VG) from an input image and subsequent detail refinement using pretrained image-to-3D model.
3D regularization (e.g., geometry priors multi-view constraints), this results in locally optimal solutions per view that can conflict globally. While yielding impressive outcomes, this strategy may not fully exploit 3D spatial cues, potentially leading challenges such view inconsistency in the final 3D vector representations.
3 Methodology
3.1 Overview
In this section, we introduce ViewCrafi3D (VC3D), optimization based method that creates a 3D vector graphic S3D based on an input image I. define a 3D vector graphic S3D as a set of 3D Bézier curves {C,) The curves are defined by set of control points {P { where P.. E R the j-th control point of the i-th curve.
Our method workflow is illustrated in Figure It begins by reconstructing a 3D mesh M from user-provided image using an image-to-3D model [5]. We then apply a two-stage process on the resulting mesh, consisting of Bézier curve fitting followed by detail refinement. The primary structure fitting stage identifies high-curvature regions in the reconstructed mesh, converts them into point cloud, and fits Bézier curves to approximate these structures. The detail refinement stage re-initializes additional curves in regions overlooked during the first stage and optimizes them using Score Distillation Sampling(SDS) loss (24), leveraging priors from the diffusion model to guide the optimization and enhance fine-grained details the resulting 3D vector graphic representation. This two-stage approach ensures both structural accuracy and high- fideeiiyy detail preservation.
3.2 Stage I: Primary Structure Fitting
In the first stage, the model extracts key structural information from the reconstructed mesh M and uses it to fit 3D Bézier curves. The fitting process is further optimized using specially designed Chamfer Distance loss.
3.2.1 Salient Point Cloud Extraction
To identify high-curvature regions on the mesh, we adopt the Sharp Edge Sampling (SES) process from Dora 4 to extract salient point cloud. We traverse each edge the mesh. For each edge, if it belongs to two adjacent faces, we compute the angle between the normal vectors of these two faces. If the angle is below a predefined threshold, we consider this edge as salient edge. To address the challenge of extracting salient edges from smooth surfaces (e.g., spheres), we uniformly sample camera parameters on horizontal plane. For each sampled viewpoint, we compute the front faces and back faces. Edges shared by front face and back face are identified as silhouette edges. All such silhouette edges are subsequently incorporated into the salient edge set. After identifying salient edges, sample points along these edges create point cloud P. ground truth.

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Ottenation Vector
Unselected Points
Point in Chuster
Previous Point mn Chuster
Cancidatt Point
(a) Initial Cluster
(b) Fist Expension
(c) Second Expansion
Final Cluster
Figure 4: Visualization of point cloud clustering. Each point is assigned orientation vector (blue arrows). In (a), the orange point initializes the cluster, and a candidate (red) is evaluated based spatial proximity and orientation similarity. In (b), the candidate meets both criteria and is incorporated into the cluster. This process iterates until the final cluster is formed, as shown (d).
3.2.2 Point Cloud Clustering
After obtaining the salient point cloud Ps. we aggregate these discrete points into clusters suitable for Bézier curve fitting. Inspired by EdgeGaussians B] we perform clustering for edge fitting based on vertex orientations. While EdgeGaussians directly utilize the principal directions of 3D Gaussians as orientation vectors, such directional information is absent in our discrete point cloud Pa.. To address this, we introduce an initialization step to estimate orientation vectors for each point in P., Specifically, for each point p. we first identify its & nearest neighbors and then apply Principal Component Analysis (PCA) 22 to the local neighborhood. The resulting primary eigenvector is used approximation of p's orientation vector Ep.
Once orientation vectors are assigned each point, we partition the point cloud into multiple elusters using an iterative expansion process shown in Fig 4 Each cluster is initialized from randomly selected starting point and grows by progressively incorporating neighboring points that satisfy both spatial proximity and orientation similarity. Specifically, we use the most recently added point in the cluster, denoted as p, to guide the selection of the next candidate. candidate point q is added the current cluster if it meets two criteria: (1) it lies within the spatial neighborhood of the cluster, i.e., dis(p, q) S d and (2) the direction of the new edge formed by and q aligns with the orientation vector of g. i.e., arccos pij can be incorporated under these constraints, completing one cluster.
ftheehh- The expansion continues until no additional points
This process repeats across the point cloud generate complete set of directional clusters, each representing potential curves for subsequent Bézier fitting. The randomized clustering method ensures comprehensive coverage of all geometric features while avoiding bias toward specific regions. Finally, we filter the small clusters with fewer than points, as they are likely to be noise or outliers.
3.2.3 Bézier Curves Fitting
After obtaining these clusters, we attempt to fit them with either straight lines or Bézier curves, selecting the one with the least error as the fitting result for that cluster.
The fitting process is performed using the Chamfer Distance loss function 26 to minimize the distance between the Bézier curves and the salient point cloud Ps obtained in Sec. 3.2.1 The fitted Bézier curves are denoted as [C For our implementation, we use cubic Bézier curves with four control points Po, Pı, P2, P3 E TR3 The parametric equation for 3D cubic Bézier curve be written in explicit representation as:
B(I) (1 1) 3(1 311-1) + - P)
t [0,
(I)
To generate a point cloud from set of Bézier curves {Ci)a |. we uniformly sample points along each curve by evaluating the parametric function (t) 1=1 for 1....,.. The resulting point cloud P. is defined as:
5

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P. = (Bi(t) l ie un....,,), JE {1)...,8)).
(2)
Chamfer Distance loss computed as follows:
Cc(Pe. Ps)
X Pa
Σ PEP-
lp ,EP,
min
all
1 [Pal
min Ilp gll Σ OEP, PEP-
(3)
where Pa denotes P. augmented with Gaussian noise (introduced for data augmentation), and À hyperparameter to balance the two terms. The generation point cloud P.. which relies on the Bézier curve formulation in Eq. [ is differentiable with respect to the curve's control points. Consequently, the chain rule enables the gradients from Le to propagate back to these control points, facilitating their iterative optimization.
3.3 Stage II: Detail Refinement
Some objects may contain prrricte-tt-approxxmme regions that Stage might miss due to limitations in the salient point cloud extraction clustering process. These regions identified by analyzing the mesh's ss vertex distribution and locating areas not adequately covered by the point cloud P. generated in Stage For such cases, we introduce additional refinement stage handle these regions by distilling priors from a pretrained image-to-3D model.
First, the parameters of the initial curves {C from Stage I are frozen. We randomly initialize new Bézier curves {C; (with parameters (*) in regions that are intricate approximate, thereby complementing the primary structure. To jointly represent both curve sets, we sample a combined P. U Par and encode into latent space using a pretrained VAE encoder from [15] z = (PPoomiined). Then we refine only the new parameters 0' via SDS loss 24].
point cloud P Poombined
supervised by the input image I.
Oz 00'
Ve LSDS = Ee,e (o(() (co(21, 1:1) c)
where 2, is the noised latent variable at timestep i, Es is the denoising model conditioned on and w(t) is a weighting function. This process iteratively adjusts the newly added Bézier curves to fill missing details from Stage I, ensuring consistency and high fidelity in the final 3D vector representation S3D
{C U (CC)'"
3.4 3D Vector Graphies Rendering
To enable both qualitative visualization and quantitative evaluation, 3D vector graphic 53D should be projected onto 2D plane. As proved by 3Doodle (5), the perspective projection of 3D Bézier curve onto 2D plane yields 2D rational Bézier curve. Given a 3D curve Btt) and image plane at (where is the depth axis and f the focal length), the projection is:
1 (z) B.te)
B2D
(t) B.C
Consequently, by explicitly defining the camera parameters 3D space, we obtain consistent set of 2D rational Bézier curves that correspond directly to the camera's viewpoint. These curves can then faithfully rendered using DiffVG [4 generate the corresponding SVG files, which are subsequently utilized for both comprchensive quantitative metrics and detailed qualitative analysis.

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Experiment
4.1 Implementation Details
Our VC3D framework is implemented PyTorch. For primary structure fitting stage, we set the distance threshold duressh = 0.05 and angle threshold Otrees = 50% Each Bézier curve is defined by 4 control points, with s 64 sample points per curve for optimization. For detail refinement stage, we employ TripoSG [5] as our pretrained image-to-3D model, with SDS loss weight set to 2 10 10-4. We use the SGD optimizer
(29 with a learning rate of 10-3
All experiments are conducted on single NVIDIA RTX 4090 GPU, For each input, our method typically produces fewer than 100 Bézier curves. Our full method takes about 30 minutes generate a vector graphic, with 100 optimization steps for Stage I and 200 steps for Stage II. We collected 40 images from prior works and online sources as imputs. All generated 3D vector graphics are rendered into 12 views using identical camera parameters, upon which the metrics are computed.
4.2 Experimental Results
We compare our approach with two sfateoff-thear methods in 3D vector graphics generation: Diff3DS [58]. which designs depth-aware differentiable rasterizer and leverages 2D diffusion model priors through SDS loss to generate 3D vector graphics from text images, and 3Doodle (5). which employs perceptual losses with multi-view guidance to obtain 3D Bézier curve representations of objects. To comprehensively evaluate the quality and fidelity of the generated 3D vector graphics, we employ CLIPScore (28) to measure semantic alignment between rendered views and input images Furthermore, we use an aesthetic indicator [3] quantify the aesthetic value.
4.2.1 Qualitative Evaluation
Figure 5 presents qualitative comparisons between method and previous work, 3Doodle [5] and Dift3DS [58). As shown, VC3D produces cleaner, more accurate, and more view-consistent 3D vector graphics. Previous works struggle capture fine details in reference images, such as the patterns on butterfly or the handle the coffee cup. Additionally, their outputs often contain excessive messy lines (e.g., g., the chair example). Figure shows the rendering results of our generated results from different camera viewpoints.
4.2.2 Quantitative Evaluation
Table presents the quantitative analysis results of all methods. Our method outperforms previous approaches in CLIPScore and Aesthetic Score. Our method achieves a cosine similarity of 0.799, which higher than the 0.729 achieved by 3Doodle and the 0.673 achieved by Diff3DS. At the same time, we achieved the highest score in Aesthetic Score. These superior results demonstrate our method's ability to generate semantically and geometrically superior 3D vector graphies.
In addition to the metrics mentioned above, our method demonstrates significant advantages in generation time. Our method requires only few SDS loss optimization steps, significantly reducing generation time. The total runtime for two stages is approximately 0.5 hours, showing notable improvements compared 3Doodle (-6 hours) and Dift3DS (-2 hours).
Table 1: Quantitative comparison of VC3D and previous methods on evaluation metrics. The bold numbers represent the best performance.
Table 2: Ablation study results comparing differ- ent variants of our proposed method. The bold numbers represent the best performance.
Aesthetic Score f
Aesthetic Score
Method
CLIPScore
Method
CLIPScore f
3Doodle
0.729
4.122
Variant
0.779
4.096
Diff3DS
0.673
3.769
Variant
0.805
4.167
VC3D (Ours)
0.799
4.352
Full Method
0.818
4.297
7

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Input Image
3Doodle
C 06hhh
Diff3DS
'--2h
VC3D (Ours)
C -0.5h
Figure 5: Qualitative comparison of different methods. Diff3DS and VC3D use single image I as input, while 3Doodle uses 120 rendered images of the mesh reconstruction result M as input.
Figure 6: Multi-view results generated by method VC3D.
4.3 Ablation Studies and Analysis
To demonstrate the respective contributions of the Chamfer Distance loss and SDS loss, we performed ablation experiments. We selected subset of 20 images from the inputs in Section 4.2. where all examples were optimized with SDS loss. And results were recorded after three distinct stages, corresponding to three variants: (1) Variant 1: This variant refers to the model with the salient point cloud extraction and the point cloud clustering. (2) Variant 2: This variant the model with the first stage only, i.e., Primary Structure Fitting, and (3) Full Method: This is our proposed method, which comprises two stages. The results are shown in Table 2 The improvements of the Variant against the Variant 1 indicate the benefits brought by using CD loss for optimization. Comparing the performance of Variant 2 and Full Method, it is clear that the detail refinement stage can further improve the performance in CLIPScore and Aesthetic Score metrics.
In Figure 7 we show the optimization process of Chamfer Distance loss. The initially fitted Bézier curves often fail to accurately cover the salient point cloud P.. The coherence between curves is
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Step 0
Step 50
Step 100
Step
Step 50
Step 100
o
0 ) 2
Figure 7: Illustration of the optimization process using Chamfer Distance loss Le-
Table 3: Ablation study results of only SDS loss. The bold numbers represent the best performance.
Method
SDS-only
Full Method
CLIP score
0.622
0.799
Aesthetic Score 1
3.653
4.352
also suboptimal. As optimization progresses, the curves gradually extend to form more complete structures, ultimately achieving both improved coverage of the salient features and enhanced inter- curve coherence while preserving the overall geometric fidelity of the original shape.
Primary Structure Fitting (Stage D) is critical component that significantly enhances both generation quality and efficiency. To demonstrate its importance, we conduct an ablation study by removing Primary Structure Fitting and solely relying on SDS loss for optimization. Results are shown in Table 3 It can be observed that the CLIPScore decreases from 0.799 to 0.622, and the Aesthetic Score drops from 4.352 to 3.653. These reductions signify marked deterioration in the visual quality of the generated outputs. Moreover, the SDS-only optimization extremely inefficient, requiring approximately 3 hours to generate vector graphic. Similar results also observed in related works like DiffSketcher 45 (Figure 8). Therefore, Primary Structure Fitting is an indispensable step that provides the necessary structural foundation for the refinement stage.
The visual improvements brought by the SDS loss can observed in Figure & where the refinement stage compensates for previously overlooked details (e.g.. terminal branches corals) and improves the structural coherence the 3D vector graphics.
Ingut Image
w/o SDS Loss
SDS Loss
Ingut Image
w/o Loss
SDS Loss
Figure 8: Illustration of the optimization effect SDS loss Leds- SDS loss effectively recovers
missing structural information while enhancing geometric detail representation.
These results demonstrate that our two-stage approach effectively balances structural accuracy with visual quality, leading to more compelling and semantically accurate 3D vector graphics.
We also experimented with the number of Bézier curves. Since the number of Bézier curves is equal to the number of point clusters, we can control the number of curves by adjusting the cluster count, i.e., by changing the filtering threshold т Point Cloud Clustering stage. As shown in Figure when = 10, point clusters with fewer than 10 points are removed. Increasing the threshold eliminates more clusters, reducing the number of Bézier curves retained and producing an abstract result. We conducted experiment using ten relatively complex examples selected from those

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used in quantitative evaluation. The quantitative results are presented in Table We can see with the filtering threshold т iccrasse. the CLIP score and Aesthetic score decrease, suggesting that geometric details are gradually reduced.
Input Image
t 10
t 20
30
= 40
Figure 9: Effect the filtering threshold in Point Cloud Clustering. The number of Bézier curves gradually decreases increases, producing abstract effect.
Table 4: Ablation study results of different The bold numbers represent the best performance.
5
10
20
30
40
CLIP score t Aesthetic Score f
0.7523
0.7676
0.7544
0.7340
0.7214
4.3918
4.3567
4.2349
4.1393
4.0580
5 Limitations and Future Works
While VC3D efficiently generates view-consistent 3D vector graphics, it is noted that individual curves may inadvertently traverse distinet semantic boundaries. Future work could address this limitation by incorporating 3D segmentation results as supervisory signal. This approach would refine the curve initialization process, ensuring the generated curves align accurately with the underlying part structures of the object.
In addition, considering that our method can generate corresponding 3D vector graphics from meshes with minimal time cost, we can build 3D vector graphics datasets based on open-source mesh datasets in the future, providing research foundation for subsequent work.
6 Conclusion
In this paper, we present VC3D, a novel framework for generating view-consistent 3D vector graphics using 3D priors. Operating directly in 3D space rather than 2D projection planes, our approach effectively addresses view inconsistency issues. Our two-stage algorithm first identifies salient structures through geometric clustering and Bézier curve fitting, then refines results using SDS loss with a pretrained image-to-3D model. Experiments demonstrate that VC3D preserves geometric characteristics while maintaining view consistency across viewpoints, with advantages in generation efficiency. This research makes high-quality 3D vector graphics creation more accessible and applicable virtual reality, shape retrieval, and conceptual design.
7 Acknowledgments
This work was supported in part by the National Key Research and Development Project of China (No. 2022ZD01 17801) and Young Elite Scientists Sponsorship Program by CAST, in part by National Natural Science Foundation of China NN..62570339, No.62461 160331, No.62132001) in part by Zhejiang Provincial Natural Science Foundation of China under Grant (No. LQN26F02004 I), in part by the Fundamental Research Funds for the Central Universities. This work also supported by the NSFC/RGC Collaborative Research Scheme (CRS HKU703/24). Dr. Xu's research work deseribed in this paper conducted in the JC STEM Lab of Multimedia and Machine Learning funded by The Hong Kong Jockey Club Charities Trust.
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Technical Appendices and Supplementary Material
Overview
This supplementary material is organized into several sections that provide additional details and analysis related to our work on ViewCraft3D (VC3D). Specifically, it includes the following aspects:
Section
we provide detailed implementation information for VC3D.
Section
we present additional qualitative results generated by VC3D.
In Section
We compared VC3D with the concurrent work Dream3DVG.
In Section
D
we describe user study that demonstrates the superiority of our method
compared to existing approaches.
In Section we discuss the applications of VC3D and 3D SVGs.
In Section
we discuss the potential societal impact of VC3D.
A Implementation Details of VC3D
A.1 Algorithm Flow of Salient Point Cloud Extraction
Algorithm shows the details of Salient Point Cloud Extraction (in main paper Sec. 3.2.1).
Algorithm 1: Salient Point Cloud Extraction
Input: Mesh file M, Boolean variable enable_find enoole_fiid_sslhoveeeeeeedee
Output: Salient point cloud P.
P. 0
// Initialize the salient point cloud
P. SES Process(M);
// Extract salient points using SES
seaheesttee_eggss enable find silhouette edges then
E Find Silhooutte Edges(MM;
// Extract silhouette edges // Sample points on silhouette edges // Merge point cloud // Return the salient point cloud
P. -
P. P.UPa:
return P.;
SES stands for Sharp Edge Sampling, technique proposed in Dora for extracting salient point clouds. The parameter enable_find_ wiwlssscetteeeeeeee saable_iin__sihhuutteee edges a whether silhouette edges are detected. We this parameter True when the mesh contains many smooth surfaces (e.g., spheres, cylinders). Figure T0 illustrates the results on the same example with enable_fine fite_fiiidssshlllllleeeee wilhooelleeeedgeeee edges both True and False for comparison.
that controls
Input Image
w/o silhouette edges
w/ silhouette edges
Effect of Silhouette Edge Extraction. Comparison of outputs with (right) and without (middle) the silhouette edge extraction enabled.
Figure 10:
When the parameter enable_find iinnole__iid_iihheeeeeeee wwthhheelhheeeeeee _edges is set to False, the salient edge extraction fails to capture the outline of the cup body, resulting in an incomplete representation. In contrast, enabling this parameter (i.e., setting it to True) allows the extraction process to effectively highlight the cup body, which is clearly reflected in the final result.
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A.2 Details of Detail Refinement
In the Detail Refinement stage, we randomly add N Bézier curves pooectte-o--pppxximale regions (as defined in Sec. 3.3 of main paper). By default, N is set to 128, but it can be adjusted based on the geometric complexity of missing areas. The topppototttoaappooimmtttt regions are determined by the mesh vertices and the point cloud P.. Specifically, for point if its distance to all points in the point cloud Ps exceeds threshold (set to 0.03 in our experiments), it is considered belong to the parricate-t-approxiiee randomly selected to initialize the Bézier curves.
regions. All such points comprise set P:, from which vertices are
For both the Bézier curves from the first stage and those newly added in the second stage, we sample 64 points per curve construct the combined point cloud Poombined. Based the mesh MM generated in the first stage, each point in Poombined is assigned approximate normal direction as geometric prior. Both Peombined and corresponding normal directions are then encoded using the VAE encoder of TripoSG [15] to generate structured latent representations. To ensure stable optimization, the SDS loss [25] employs medium-strength noise with the range [0.15, 0.4) and multiple denoising steps. This strategy effectively balances global shape consistency with local detail preservation, thereby enhancing the visual quality of the generated vector graphics.
B More Qualitative Results
B.1 More Qualitative Results of VC3D
As shown in Figure II VC3D is capable of generating high-quality results across diverse range of 3D object categories, such animals, industrial products, and furniture.
Figure 11: More qualitative results generated by our method VC3D.
B.2 More Comparisons with Other Methods
Figure 12 presents additional comparisons between method and baseline methods. It can be observed that VC3D outperforms these methods terms of fidelity and view-consistency.
23

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Imput Image
3Doddle
Diff3DS
VC3D (Ows)
Figure 12: More qualitative comparisons between our method and baseline methods, 3Doodle and Diff3DS.
C Comparison with Dream3DVG
We conducted quantitative comparison with Dream3DVG [52]. Specifically, given that Dream3DVG accepts text prompts as input, manually convert the input images from our quantitative experiments into text prompts and use them as input for Dream3DVG. As shown in Table 5 our method achieves higher aesthetic score than Dream3DVG.
Table 5: Quantitative comparison of VC3D and Dream3DVG 52 best performance.
The bold numbers represent the
Method
VC3D (Ours)
Dream3DVG
Aesthetic Score
4.352
4.1499
D
User Study
We conducted user study to evaluate the quality of the synthesized 3D vector graphics. The test used in the study identical that in the main paper and consists of 40 cases, each with results generated by three different approaches. For the user study, each questionnaire contained 20 cases randomly sampled from these 40.

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We invited 30 volunteers to participate in the study, with gender ratio of upproximately 5:1 (male female). Most participants are university students from various science and engineering disciplines, with ages ranging from 18 to 27. Volunteers were asked to choose the best result for each case based on two criteria: fidelity and view-consistency. As
VC3D
71.17%
13.83%
shown in Fig. 13 our approach received 71.17% of the votes, outperforming Dooddle (13.83%) and
3Doodle
Dift3DS (15%).
15.00%
E Applications
Diff3DS
of VC3D and Resultant 3D SVGs
Figure 13: The results of the user study.
The inherent editability of 3D vector graphics of- fers significant advantages for iterative modeling workflows, a potential explored in several aca- demic works. (1) 3D vector curves can serve as real-time guidance for surface creation and editing. For instance, FiberMesh [21] enables the design of freeform surfaces by allowing users to directly draw and manipulate network of 3D curves, which function structural vector scaffold for the surface. (2) Some works focus on reconstructing 3D surfaces from network of 3D vector curves, treating the curve network an editable and interpretable representation that facilitates intuitive shape refinement. Notably, True2Form [48 infers complete 3D curve network from ambiguous 2D sketches, bridging the gap between sparse 2D input and structured 3D geometry.
3D vector graphies also support animation production. For example, Blender provides official SVG plugin and keyframe interpolation tools. Users can project 3D vector models onto different viewpoints, and then generate unimations using the plugin. This enables the creation of 2D animations from multiple angles while maintaining strong frame-to-free fo-frame consistency.
3D vector graphics integral to VR/AR applications, and they have already been applied in some well-known software, such as Tilt Brush, Gravity Sketch, and Mental Canvas. With the rapid development of VR/AR devices and applications, 3D vector graphies will become more valuable. Our proposed approach can be used to generate 3D vector graphics assets.
Societal Impact
Our method, VC3D, significantly enhances the fidelity and vie-conssytencyy of 3D vector graphics generated from single image. However, as with other generative models, there exists critical concern regarding the potential negative societal impact if our model misused to create inauthentic or misleading 3D vector content. It is imperative to ensure that the model is used responsibly to mitigate the risk of adverse social consequences.
25


Reviews:
[{"review_id": "9JILDZAabd", "reviewer": "Reviewer_ztRZ", "summary": "The paper presents a method to generate view-consistent 3D vector sketches based on an input image. The main idea of the method is to combine geometric fitting and 3D-prior guided refinement. The geometric fitting stage includes an image-to-3D model process, salient point cloud extraction, point cloud clustering, and Bézier curves fitting. This stage looks like an edge extraction process for a 3D model. In the 3D-prior guided refinement, a 3D SDS loss is used to guide the curve refinement process for regions overlooked in the first stage.", "questions": "1. Please clarify the technical novelty of the paper.\n2. Please add comparisons with Dream3DVG [47].\n3. Please discuss practical applications of the approach.", "limitations": "The practical applications of 3D sketches and the generation of them should be added and discussed.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths:\n1.  The idea of combining traditional geometric fitting and 3D-prior guided refinement is interesting, and I think the former is key to maintaining 3D structure and view consistency.\n2.  The writing is good. The paper is easy to read.\n3.  The experiments are sufficient, including comparisons with existing approaches and ablation studies.\n\n# Weaknesses:\n1.  Although the idea of the paper is very interesting, the technical side looks straightforward and incremental by combining existing techniques.\n2.  The comparisons with Dream3DVG [47] are lacking, as it also works on consistent views.\n3.  I am curious about the practical applications of the proposed approach and the output results. The authors mentioned virtual reality creation in the introduction. I would like to see such applications and examples in the paper.", "was_revised": true, "final_justification": "Dream3DVG is indeed a concurrent work, and it is not necessarily essential to this one; the practical applications of the approach are still relatively narrow, but as a research topic, it is acceptable. Therefore, I agree to accept this paper.", "rebuttal": "### **W1 & Q1: Novelty.**\n\nOur core contribution is the introduction of a **novel framework that directly addresses the fundamental issue of view-inconsistency** prevalent in existing 3D graphics generation methods. Previous works, such as 3Doodle[1], Diff3DS[2], and Dream3DVG[3], rely heavily on **2D constraints**: they project 3D gaphics into 2D views and utilize differentiable rendering to enforce alignment between the projections and image ground-truth. However, this reliance on 2D priors inherently limits their ability to enforce true 3D coherence across multiple views.\n\nIn contrast, **our method introduces a paradigm shift by incorporating 3D priors directly into the optimization process for 3D vector graphics.** Specifically, we extract salient edges from the mesh and leverage a pretrained 3D generative model to define a 3D SDS loss in latent space, thereby completely removing the dependency on 2D projections. This enables optimization to be guided by a **holistic understanding of 3D shape geometry**, rather than by enforcing consistency across 2D views.\n\nTo the best of our knowledge, **ViewCraft3D is the first framework to apply 3D SDS loss for optimizing structured 3D vector graphics**, which introduces both conceptual and technical novelty beyond a simple combination of existing components.\n\n### **W2 & Q2: Comparison with Dream3DVG.**\n\nWe should clarify that Dream3DVG is **concurrent work**, since its preprint was released (May 27) after the NeurIPS submission deadline. Therefore, the absence of this comparison in our initial submission is not a weakness. Nevertheless, we have conducted a quantitative comparison. Specifically, given that Dream3DVG accepts text prompts as input, we manually convert the input images from our quantitative experiments into text prompts and use them as input for Dream3DVG. As shown in the following table, our method achieves a higher aesthetic score than Dream3DVG.\n\n|                  | VC3D (Ours) | Dream3DVG |\n|------------------|-------------|-----------|\n| Aesthetic Score↑ | 4.352       | 4.1499    |\n\n\nIn additional to the quantitative analysis, we also conducted a qualitative comparison between our method and Dream3DVG on two representative categories of objects. **Given the format restrictions that no addtional images can be added during the rebuttal phase, we describe the results below**:\n\n(1) For **man-made objects** with strong symmetry and straight-line dominance, such as cars, pistols, and houses, Dream3DVG often produces results resembling a 3D version of CLIPasso. **It tends to omit or oversimplify parallel structural lines representing object thickness.** For instance, vertical lines indicating the walls of a house are frequently missing. We hypothesize this is due to misleading optimization signals: parallel structural lines that should be generated simultaneously may appear overlapping or closely spaced in certain viewpoints, causing the optimization process to favor only one of them and ignore the rest.\n\n(2) For **natural objects** with asymmetric and richly curved structures, such as birds, butterflies, and flowers, **Dream3DVG suffers from more pronounced view inconsistency issues**. Taking the bird example (our result shown in Figure 1 of the main paper), Dream3DVG generates duplicated features from different viewpoints. For example, the beak and tail feathers appear twice, and an extra eye is hallucinated on the back of the head. These results demonstrate Dream3DVG's challenge in maintaining coherent and unified vector abstraction across diverse views of natural objects.\n\nThese comparisons suggest that while Dream3DVG performs reasonably well on simple and rigid structures, it struggles with both the **geometric completeness** required for structured man-made objects and the **multi-view consistency** critical for deformable, natural categories. We will include the above results and discussion in the revised version.\n\n### **W3 & Q3: Applications of Our Method and Resultant 3D SVGs.**\n\nThe inherent editability of 3D vector graphics offers significant advantages for iterative modeling workflows, a potential explored in several academic works. (1) 3D vector curves can serve as **real-time guidance for surface creation and editing**. For instance, FiberMesh[4] enables the design of freeform surfaces by allowing users to directly draw and manipulate a network of 3D curves, which function as a structural vector scaffold for the surface. (2) Some works focus on reconstructing 3D surfaces from a network of 3D vector curves, treating the curve network as an **editable and interpretable representation that facilitates intuitive shape refinement**. Notably, True2Form[5] infers a complete 3D curve network from ambiguous 2D sketches, bridging the gap between sparse 2D input and structured 3D geometry.\n\n3D vector graphics also support **animation production**. For example, Blender provides an official SVG plugin and keyframe interpolation tools. Users can project 3D vector models onto different viewpoints, and then generate animations using the plugin. This enables the creation of 2D animations from multiple angles while maintaining strong frame-to-frame consistency.\n\n3D vector graphics are integral to VR/AR applications, and they have already been applied in some well-known software, such as Tilt Brush, Gravity Sketch, and Mental Canvas. With the rapid development of VR/AR devices and applications, 3D vector graphics will become more valuable. Our proposed approach can be used to generate 3D vector graphics assets.\n\n\n[1] 3Doodle: Compact Abstraction of Objects with 3D Strokes\n\n[2] Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering\n\n[3] Empowering Vector Graphics with Consistently Arbitrary Viewing and View-dependent Visibility\n\n[4] FiberMesh: Designing Freeform Surfaces with 3D Curves\n\n[5] True2Form: 3D curve networks from 2D sketches via selective regularization"}, {"review_id": "lq8dlCAauz", "reviewer": "Reviewer_X23k", "summary": "This paper proposes ViewCraft3D, a pipeline for generating 3D parametric curves from a single image. VC3D first generates a 3D mesh using a pretrained image-to-3D generator. Then, salient points are extracted from the mesh, and heuristic point cloud clustering is applied to the sampled points to form clusters that constitute potential curves. Subsequently, the authors propose Bezier curve fitting to optimize the parameters of cubic Bezier curves, using the Chamfer distance between the original sampled points and resampled points from the curves as the optimization objective. SDS loss is also introduced as a refinement step to enhance detail. Quantitative experiments using CLIP score and aesthetic score as metrics demonstrate improvements over previous methods. VC3D also shows a significant advantage in optimization time.", "questions": "Questions:\n\n1.\tWhether and how can VC3D optimize concise yet globally informative long strokes? (Regarding Weakness 2)\n\n2.\tWhat would results using only SDS loss yield? Does SDS loss in Stage II contradict Phase I optimization? (Regarding Weaknesses 3 & 4, this is crucial for evaluating the contribution.)\n\n3.\tCould VC3D be adapted to other 3D generation frameworks other than Triposg?", "limitations": "Unable to reconstruct textures, and generated line segments appearing fragmented. Details refer to Weaknesses.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1.\tThis paper is well-written and easy to follow.\n\n2.\tAlthough the proposed method is straightforward, the numerical experiments demonstrate its effectiveness to some extent (but I still have some questions according to the provided qualitative results).\n\n3.\tThe experiments are sufficiently comprehensive, with most tests effectively demonstrating the characteristics of different components introduced.\n\nWeaknesses:\n\n1.\tTexture cannot be reconstructed, only shape can be recovered. For example, the leopard's skin spots in Figure 8 fail to be reconstructed. This is likely to be limited by the pretrained image-to-mesh generator [1].\n\n2.\tThe Point Cloud Clustering proposed in Stage 1 is too simple, and seems cannot handle complex, global lines (longer strokes) in images. This is evident in Figure 7 and Figure 8 (e.g., τ = 40). Compared to Figure 1 in CLIPasso [2], which uses fewer but more conclusive strokes, Point Cloud Clustering produces fewer continuous lines to outline objects.\n\n3.\tRegarding Figure 7 (wo SDS), Primary Structure Fitting only generates initial structures, while SDS loss *mainly* ensures visual similarity between reconstructed 3D vectors and inputs. This may diminish the paper’s claimed contribution as it seems that SDS is more important. An ablation study (e.g., SDS-only results) should visually clarify Primary Structure Fitting’s role.\n\n4.\tAdditionally, SDS employs $\\epsilon_{\\phi}(z_t, I; t)$, relying on a point cloud-based pre-trained distribution (spread across surfaces) [1]. Could this contradict the previously extracted salient points (which are probably the edge points)?\n\n[1] Triposg: High-fidelity 3d shape synthesis using large-scale rectified flow models\n\n[2] CLIPasso: Semantically-Aware Object Sketching", "was_revised": true, "final_justification": "The authors have addressed my concerns in the rebuttal, particularly by further elaborating on the feasibility of using TripoSG as SDS guidance, which demonstrates the soundness of the proposed approach. Therefore, I am inclined to believe that this work should be accepted.", "rebuttal": "### **W1: Texture Synthesis.**\n\nThank you for this insightful comment. The absence of texture is an intentional design choice, as our paper's core objective is to generate view-consistent 3D vector graphics by leveraging 3D geometric priors. Our method focuses exclusively on these geometric features to solve the central challenge of view consistency for an object's structure. We agree that incorporating texture is an important direction, and we will explore it in our future work.\n\n### **W2 & Q1: Clustering Method and Ability of Producing Long Strokes.**\n\nOur clustering method is capable of generating long and continuous strokes, as demonstrated by the global outlines of the *coffee cup* and *scissors* in Figure 5. Unlike 2D abstraction approaches such as CLIPasso, which are optimized for perceptual minimalism, our task requires balancing coherence across multiple viewpoints with localized geometric detail. Therefore, there is a trade-off between stroke length/number and detail preservation.\n\nSpecifically, for objects with rich details, lowering hyperparameter $\\tau$ retains more clusters, yielding more Bézier curves and richer details. For objects best described by long strokes, increasing hyperparameter $d_{thresh}$ encourages each cluster to extend longer, thereby yielding longer Bézier curve fitting results.\n\n### **W3 & Q2: The Role of Primary Structure Fitting.**\n\nThank you for your question. Primary Structure Fitting is a critical component that significantly enhances both generation quality and efficiency. To demonstrate its importance, we conduct an ablation study by removing Primary Structure Fitting (Stage I) and relying solely on SDS loss for optimization. The results are shown in the following table:\n\n|                  | SDS-only | Full Method |\n|------------------|----------|-------------|\n| CLIPScore↑       | 0.622    | 0.799       |\n| Aesthetic Score↑ | 3.653    | 4.352       |\n\n\nWe can see that the CLIPScore decreases from 0.799 to 0.622, and the Aesthetic Score drops from 4.352 to 3.653. Additionally, we perform qualitative comparison and observe a noticeable degradation in 3D view consistency compared to our full method. The visualization results show that the lines in SDS-only generated results are more cluttered and lack sufficient structural lines. Moreover, the SDS-only optimization is extremely inefficient, requiring approximately 3 hours to generate a vector graphic. Similar results are also observed in related works like DiffSketcher[1] (Figure 8). Therefore, Primary Structure Fitting is **an indispensable step that provides the necessary structural foundation for the refinement stage**. We will include this ablation study in the next version.\n\n### **W4: Potential Contradict.**\n\nWe appreciate the reviewer’s insightful observation regarding the potential contradiction between the point cloud prior used in SDS and the salient edge-based curve fitting in Stage I.\n\nAs we expalined in Line209-211 of our submission, the final 3D SVG composite of two sets of Bezier curves, curves from Stage I and II. Specifically, curves from **Stage I** fit to salient edges, capturing the primary structural features of the mesh. At **Stage II**, we initialize new curves in regions that are geometrically complex but not well approximated by Stage I, such as flat or smooth surfaces with fine-scale details. This design ensures that the aggregated point cloud better aligns with the distribution expected by the pretrained model used in SDS, which is based on full-surface point clouds.\n\nMoreover, our empirical results show that the TripoSG encoder is still capable of encoding the Bézier curve-sampled point cloud into valid latents. By applying noise to the latent code and denoising through the Rectified Flow model, we can reconstruct the overall 3D shape effectively. This demonstrates that, although our point cloud is sampled differently, it still falls within the distribution that TripoSG can effectively encode.\n\n### **Q3: Adapt to Other 3D Generation Frameworks.**\n\nOur VC3D can be adapted to other 3D generative frameworks beyond TripoSG. The key requirement is that the framework must **provide an encoder**. This encoder is essential for our method, as it takes the point cloud sampled from the 3D Bézier curve representation and encodes it into latent space, which is then used for optimization via Score Distillation Sampling (SDS). While other frameworks, such as Hunyuan3D[2], are potential candidates, many have only released the decoder code. Therefore, we selected TripoSG as it offers a publicly available encoder that fulfills this requirement and also demonstrates strong generation performance.\n\n[1] DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models.\n\n[2] Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation"}, {"review_id": "jbOOOrM8OS", "reviewer": "Reviewer_vumt", "summary": "The paper introduces ViewCraft3D (VC3D), a multi-stage method for generating high-fidelity, view-consistent 3D vector graphics by incorporating 3D priors directly in 3D space. First, it extracts salient edges from meshes to create a point cloud, which is clustered using KNN and PCA for Bézier curve fitting. Second, it refines details by adding and optimizing additional Bézier curves with a Score Distillation Sampling (SDS) loss guided by a pretrained image-to-3D model. Experiments show that VC3D surpasses prior methods in CLIPScore, aesthetic quality, and runtime, achieving better view consistency and efficiency.", "questions": "1. I would appreciate a more concrete example to show how the generated 3D vector graphics could benefit sub-stream application.\n2. In the limitations, the authors mention that they cannot handle occlusion relationship. It would be helpful to include qualitative examples demonstrating these failure cases to better understand the scope of this limitation.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n1. The method is clearly presented and demonstrates improvement in performance over competitors on quantitative metrics. \n\n2. Ablation studies validate the contributions of both the Chamfer loss and the SDS loss. \n\n3. The method leverage explicit 3D geometric priors for generating view-consistent 3D vector graphics.\n\n**Weakness**\n1. The most prominent improvement on 3D consistency appears dependent on the quality of the mesh obtained from the previous image-to-3D generative model. The subsequent steps just further refine the quality of generated 3D vector graphics. \n\n2. The authors should clarify why generating a 3D vector graphics from a 3D mesh—already obtained via an image-to-3D generative model—offers a more meaningful or useful representation for the sub-stream tasks **than simply using the 3D mesh itself or the point clouds extracted from it**. My concern is the application scenario is a little bit narrow if there are only aesthetics values. As mentioned before, the proposed approach relies on meshes (already a concrete, ultimate 3D format) obtained by an additional 3D generative step to instead produce an intermediate representation (the 3D vector graphics), which seems somewhat counterintuitive as it makes the whole pipeline becomes a task-driven solution.", "was_revised": true, "final_justification": "I would suggest that the author provide more examples to illustrate the practicality. Overall, I intend to recommend acceptance of this paper.", "rebuttal": "### **W1: The Role of Reconstructed Mesh.**\n\nThank you for your question. The most critical idea of our approach is to achieve view consistency by leveraging 3D priors for 3D vector graphics generation. The 3D priors include geometric priors from the reconstructed mesh (Stage I) and generative priors from a pretrained 3D generative model (Stage II). Therefore, the quality of the generated 3D vector graphics is not simply determined by the quality of the mesh.\n\nSpecifically, in Stage I, we extract only the most salient edges from the reconstructed mesh, intentionally filtering out noise and surface details to mitigate the influence of potential artifacts or inaccuracies in the mesh. In Stage II, the vector graphic is further refined via SDS optimization, using 3D generative priors learned from large-scale 3D assets.\n\nAs a result, view consistency achieved by our method is not passively inherited from the mesh, but is actively enforced through our two-stage pipeline and the integration of 3D priors.\n\n### **W2 & Q1: Application of 3D Vector Graphics.**\n\nWe appreciate the reviewer’s concern regarding the utility of 3D vector graphics beyond aesthetic purposes. Our method produces a lightweight view-consistent 3D vector representation that offers distinct advantages for downstream tasks where **interpretability, interactivity, and performance efficiency** are critical.\n\nFor example, in an **AR-based industrial assistance application**, the generated vector graphic can be overlaid on real-world machinery to provide intuitive visual guidance. Instead of rendering a full textured 3D mesh, which is often computationally expensive, the vector abstraction can highlight the outline of a component that needs replacement or trace the routing of a cable, enabling fast, responsive, and informative AR interactions, especially on resource-constrained devices.\n\nBeyond AR, our representations are also valuable in **CAD and industrial design**, where curve-based editing and structural manipulation are standard. Designers can interactively modify part outlines, reuse components, or perform topological edits without operating on dense, hard-to-parse mesh data. Similarly, in **education and training**, 3D vector graphics allow learners to better understand object structures through simplified line-based visualizations, which can emphasize functionally important parts over surface-level details.\n\nThus, our use of 3D vector graphics is not merely for aesthetics, but reflects a **deliberate design trade-off that prioritizes editability, abstraction, and semantic clarity**. Such capabilities are not inherently provided by raw 3D meshes. For more specific application scenarios, please refer to our response to Reviewer ztRZ W3.\n\n### **Q2: Occlusion Limitation.**\n\nSorry for the confusion. The occlusion limitation mentioned in the paper specifically refers to the **2D rendering of the 3D vector graphic for visualization purposes**, and is not related to the quality of the generated 3D curve representation itself. Since our current rendering assumes the same opacity and no depth ordering (i.e., all curves are drawn with the same style regardless of their spatial relationships), this can occasionally lead to reduced visual clarity and spatial interpretability, especially in cases involving dense overlaps or self-occluding structures. We will include qualitative examples of failure cases in the revised version."}, {"review_id": "0GHF7IlTVL", "reviewer": "Reviewer_221W", "summary": "The paper proposes an efficient pipeline for generating 3D vector graphics from a single image. The method first reconstructs a 3D mesh using an initial image-to-3D model, then fits vector graphics to this mesh, and finally leverages a pretrained model to refine the details, resulting in view-consistent and high-quality output.", "questions": "My main concerns relate to missing details and a lack of complexity analysis. I would reconsider my rating if the authors could clarify:\n Bézier Fitting: What determines the number of curves to fit to a point cloud? How are regions for refinement identified, and how are new curves initialized?\n Complexity: Your method seems to produce more curves than baselines (e.g., 3Doodle). Can you provide a quantitative comparison and discuss this complexity-quality trade-off?\n Diffusion Model: Can you provide more details on the architecture and operation of the diffusion model used for refinement?\nAddressing these points would significantly strengthen the paper's technical contribution and clarity.", "limitations": "Yes,", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "The authors have developed a functional and well-structured pipeline that successfully addresses the task of image-to-3D-vector-graphics conversion. The strength of the work is its clear demonstration of how to chain existing models to achieve a desirable result.\n\nHowever, the paper's main drawback is its reliance on pre-existing components. The pipeline's architecture is a straightforward combination of established techniques, and the core generative model is borrowed from another source. Consequently, the paper's fundamental technical contribution is not as significant as one might hope for.", "was_revised": true, "final_justification": "The rebuttal has addressed most of my concerns.", "rebuttal": "### **W1: Novelty.**\n\nWhile our pipeline leverages pretrained models, our core contribution is the introduction of a **novel framework that directly addresses the fundamental issue of view-inconsistency** prevalent in existing 3D graphics generation methods. Previous works, such as 3Doodle[1], Diff3DS[2], and Dream3DVG[3], rely heavily on **2D constraints**: they project 3D models into 2D views and utilize differentiable rendering to enforce alignment between the projections and image ground-truth. However, this reliance on 2D priors inherently limits their ability to enforce true 3D coherence across multiple views.\n\nIn contrast, **our method introduces a paradigm shift by incorporating 3D priors directly into the optimization process for 3D vector graphics**. Specifically, we extract salient edges from the mesh and leverage a pretrained 3D generative model to define a 3D SDS loss in latent space, thereby completely removing the dependency on 2D projections. This enables optimization to be guided by a **holistic understanding of 3D shape geometry**, rather than by enforcing consistency across 2D views.\n\nTo the best of our knowledge, **ViewCraft3D is the first framework to apply 3D SDS loss for optimizing structured 3D vector graphics**, which introduces both conceptual and technical novelty beyond a simple combination of existing components.\n\n### **Q1: Bézier Fitting.**\n\n**Number of Curves:** The number of curves is jointly determined by the number of clusters, resulting from the Point Cloud Clustering stage (Section 3.2.2), and the filtering threshold, $\\tau$. The filtering threshold removes clusters with fewer than $\\tau$ points, and each of the remaining clusters then has a Bézier curve fitted to it.\n\n**Region Identification and Curve Initialization:** As we detailed in Lines 204-214, regions for refinement are identified by analyzing the mesh's vertex  distribution, locating areas not adequately covered by the point cloud $\\mathcal{P}_c$  from Stage I (Section 3.2). Specifically, a vertex is considered  \"uncovered\" if there are no points from point cloud $\\mathcal{P}_c$ within a radius $R$ of that vertex. To initialize new Bézier curves, we first apply K-Means clustering to uniformly sample points from the uncovered point set, using them as initial control points. The remaining control points are then randomly selected within a confined local region around each initial point, forming the initial curves.\n\n### **Q2: Complexity.**\n\nThank you for the question. Our method may produce more curves. **However, this reflects the model’s ability to capture finer geometric details**. To quantitatively investigate the complexity–quality trade-off, we conducted an experiment using ten relatively complex examples selected from those reported in the main paper. The quantitative results are presented in the table below. \n\n|  $\\tau$               | 10      | 20      | 30      | 40     |\n|------------------|---------|---------|---------|--------|\n| CLIP score↑      | 0.7676  | 0.7544  | 0.7340  | 0.7214 |\n| Aesthetic Score↑ | 4.3567  | 4.2349  | 4.1393  | 4.0580 |\n\n\nWe can see with the filtering threshold τ  increased, the CLIP score and Aesthetic score decrease, suggesting that geometric details are gradually reduced.\n\n### **Q3: Details of Diffusion Model.**\n\nThe goal of the refinement stage is to improve the initial Bézier curve representation obtained in Stage I by leveraging 3D generative priors via **Score Distillation Sampling (SDS)**. SDS aims to generate a pseudo-supervisory signal from a pretrained diffusion model (in our case, an image-conditioned 3D diffusion model) and iteratively adjusts the target (here, the Bézier curve parameters $\\theta$) via gradient descent.\n\nOur method builds upon the architecture of TripoSG, which comprises two main components: **3D Variational Autoencoder (VAE) and a Rectified Flow Transformer**. In our pipeline, we first sample a point cloud from the Bézier curves and encode it into the latent space $\\mathcal{Z}$ using the pretrained VAE encoder from TripoSG, obtaining a latent code $z$.\n\nWe then apply SDS by perturbing this latent code and feeding it into the Rectified Flow Transformer, which returns a score (i.e., a denoising direction). This score acts as a gradient signal that suggests how the latent code—and consequently the underlying 3D curves—should be modified to better match the priors captured by the diffusion model. We backpropagate this gradient through the VAE encoder and the point sampling process to update the Bézier curve parameters $\\theta$, thereby refining the 3D curves in a differentiable, geometry-aware fashion. \n\n[1] 3Doodle: Compact Abstraction of Objects with 3D Strokes\n\n[2] Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering\n\n[3] Empowering Vector Graphics with Consistently Arbitrary Viewing and View-dependent Visibility"}]

Author rebuttal:

Meta-review: This paper proposes a method to synthesize 3D Vector Graphics from a single input image. The key contribution of this paper lies in the proposed techniques for generating 3D parametric curves for a 3D mesh obtained via a pretrained image-to-3D generator. After the rebuttal, all four reviewers gave the same positive score (4: Borderline accept). Moreover, all reviewers mentioned clearly in the Final Justification that they intended to accept this paper. The AC has carefully checked the authors’ rebuttal, the reviewers’ comments, and the author-reviewer discussions. The AC agrees with the reviewers and suggests the acceptance of this paper. Please follow the suggestions of all reviewers to revise the manuscript.
Decision: Accept (poster)