Paper Overview
Field: Computer Vision (CV) Authors: Xudong Cai, Ruijie Zhu, Haoliang Shang, Ruining Li, Marc Pollefeys, Shangzhe Wu, Andrea Vedaldi, Chuanxia Zheng arXiv: 2610.13947
Abstract
In this paper, we study physically grounded articulated 3D asset generation --- moving the goal from reproducing how objects look to capturing how they move and function. Inspired by the literature on procedural and agentic generation, we present Artisan, a system that extends the latest method Articraft by introducing a multi-agent pipeline with physical simulation feedback, enabling comprehensive attributes beyond geometry, appearance, and physically plausible articulation, such as collision-free motion, stability under gravity, and accurate mass and material properties.
Furthermore, we construct a unified benchmark spanning four key aspects: spatial fidelity, physical plausibility, downstream task performance, and overall quality. The benchmark encompasses 100 reference assets from ArtVIP, 50 stability prompts, and 50 ABO objects with reference mass.
Key Findings
- Extensive evaluation over ten+ representative models shows Artisan is competitive in spatial fidelity while achieving the lowest collision rate and highest stability rate.
- The system and benchmark together provide a direction for advancing 3D generation toward physically credible, functionally usable assets.