PAOLI: Pose-free Articulated Object Learning from Sparse-view Images

ArXi:2509.04276v2 Announce Type: replace We present a methodology to model articulated objects using a sparse set of images with unknown poses. Current methods require dense multi-view observations and ground-truth camera poses. Our approach operates with as few as four views per articulation and no camera supervision. Our central insight is to first solve a robust correspondence and alignment problem between unaligned reconstructions, before part motions can be analyzed.