PhysX-CoT: Structured Physical Reasoning from a Single Image to Simulation-Ready 3D Assets
This work addresses the limitations of existing methods for generating simulation-ready 3D assets from a single image, which suffer from implicit reasoning that entangles part layout with local shape and lacks supervisability over intermediate states. To overcome this, we propose an explicit, structured physical reasoning framework that models part decomposition, 2D/3D localization, inter-part relationships, coarse geometry, and surface cues sequentially through interpretable state trajectories, enabling supervision, conditional control, and optimization of intermediate steps. Our approach employs factorized decoding—using 3D bounding boxes for pose and local codes for shape—alongside a Chain-of-Thought-aligned GRPO algorithm and a frozen decoder architecture. Evaluated under a unified protocol, our method consistently outperforms baselines across geometric, scale, and physical plausibility metrics, producing assets that exhibit high-fidelity parseability, accurate collision responses, and functional articulation in Unreal Engine 5.