SPARK: Sim-ready Part-level Articulated Reconstruction with VLM Knowledge
He, Yumeng, Jiang, Ying, Lu, Jiayin, Yang, Yin, Jiang, Chenfanfu
原文摘要
Articulated 3D objects are critical for embodied AI, robotics, and scene understanding, yet creating simulation-ready assets remains labor-intensive and requires expert modeling of part hierarchies and motion structures. We introduce SPARK, a framework for reconstructing physically consistent, kinematic part-level articulated objects from a single RGB image. Given the image, we first leverage VLMs to extract coarse URDF parameters and generate part-level reference images. We then integrate the part-image guidance and the inferred structure graph into a diffusion transformer to synthesize consistent part and complete shapes of articulated objects. To further refine the URDF parameters, we explore a VLM-based reprediction strategy for discrete attribute refinement and a differentiable forward kinematics module for continuous parameter optimization under VLM-generated open-state supervision. Extensive experiments show that SPARK produces high-quality, simulation-ready articulated assets across diverse categories, enabling downstream applications such as robotic manipulation and interaction modeling.
cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表