BAT3R: Bootstrapping Articulated 3D Reconstruction from 2D Image Collections
3D reconstruction of articulated objects from a single image is challenging because large training datasets with paired image and 3D supervision are difficult to obtain. Recent point map-based methods achieve strong performance but rely on synthetic datasets rendered from manually created articulated 3D assets with carefully curated pose distributions. While camera viewpoints can be easily sampled, generating realistic object articulations remains costly and labor-intensive. We propose a training framework that reduces this requirement by leveraging unannotated 2D images collections with only a single rigged canonical mesh per category. Starting from a weak 3D shape predictor trained on c...
| Authors | Jakub Zadrozny, Oisin Mac Aodha, Hakan Bilen |
|---|---|
| Primary category | cs.CV |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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