ViewCraft3D: High-fidelity and View-Consistent 3D Vector Graphics Synthesis

Chuang Wang, Haitao Zhou, Ling Luo, Qian Yu

Keywords: Sketch Generation; 3D Vector Graphic; Diffusion Models

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.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 6373