Bootstrapping Articulated 3D Reconstruction from 2D Image Collections

3D reconstruction of articulated objects from a single im-age is challenging because large training datasets with paired image and3D supervision are difficult to obtain. Recent point map–based methodsachieve strong performance but rely on synthetic datasets rendered frommanually created articulated 3D assets with carefully curated pose dis-tributions. While camera viewpoints can be easily sampled, generatingrealistic object articulations remains costly and labor-intensive. We pro-pose a training framework that reduces this requirement by leveragingunannotated 2D images collections with only a single rigged canonicalmesh per category. Starting from a weak 3D shape predictor trained oncanonical-pose renders, we iteratively estimate object articulation andcamera pose by fitting the mesh to predicted point maps. The recov-ered articulations and viewpoints are then used to render updated syn-thetic training data, progressively improving the predictor. Despite us-ing substantially weaker 3D supervision, our models achieve performancecomparable with DualPM, which requires manually curated articulatedtraining datasets.