Artic-O: End-to-End Articulated Object Reconstruction via Latent Geometry Learning
Reconstructing articulated objects from sparse images requires recovering complete geometry, movable parts, and motion parameters. Recent methods typically separate geometry reconstruction, part reasoning, and articulation estimation into different stages. This separation can weaken consistency between shape, active parts, and motion, while also incurring substantial inference cost. We introduce Artic-O, an end-to-end, feed-forward framework for articulated object reconstruction via latent geometry learning. Instead of fitting geometry in image or view space, Artic-O maps sparse multi-state observations into a pretrained latent geometry space, where a frozen flow-matching decoder provides...
| Authors | Xuyang Wang, Zhenyu Li, Jian Ding et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 June 2026 |
| Last updated at source | 10 September 2026 |
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