TransArt3D: From Two States to Articulated 3D Reconstruction with Video Transition Priors

Chenhao Guan, Liying Yang, Xinxing Yu, Hao Mo, Tianshun Han +2

Reconstructing interactable articulated assets from a few images is key to robotics and simulation, yet highly underconstrained. Existing optimization-based methods fit an articulated asset under a static 3D prior that scores each state independently, so per-state reconstruction errors leak into the estimated moving part and joint as false motion. Video diffusion models capture how objects move between states, but only implicitly in 2D. To this end, we present TransArt3D, an optimization-based framework that reconstructs an articulated 3D asset from two endpoint images by translating this video transition prior into explicit part structure and joint kinematics. It first lifts frames generated between the endpoints with a transition-coupled 3D consensus that applies the static 3D prior jointly across frames, stabilizing unchanging regions and initializing the parts and joint. A rectified-flow kinematic refinement then lets the video prior supervise a 3D motion field over the whole transition, while part assignment and a shared joint constrain the field throughout optimization, keeping per-state errors out of the articulation. Finally, a motion-aware canonical appearance fusion…