{"ID":2826220,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.19283","arxiv_id":"2512.19283","title":"OmniEgoCap: Camera-Agnostic Sequence-Level Egocentric Motion Reconstruction","abstract":"The proliferation of commercial egocentric devices offers a unique lens into human behavior, yet reconstructing full-body 3D motion remains difficult due to frequent self-occlusion and the 'out-of-sight' nature of the wearer's limbs. While head and hand trajectories provide sparse anchor points, current methods often overfit to specific hardware optics or rely on expensive, post-hoc optimizations that compromise motion naturalness. In this paper, we present OmniEgoCap, a unified diffusion framework that scales egocentric reconstruction to diverse capture setups. By shifting from short-term windowed estimation to sequence-level inference, our method captures a global perspective and recovers invariant physical attributes, such as height and body proportions, that provide critical constraints for disambiguating head-only cues. To ensure hardware-agnostic generalization, we introduce a geometry-aware visibility augmentation strategy that treats intermittent hand appearances as principled geometric constraints rather than missing data. Our architecture jointly predicts temporally coherent motion and consistent body shape, establishing a new state-of-the-art on public benchmarks and demonstrating robust performance across diverse, in-the-wild environments.","short_abstract":"The proliferation of commercial egocentric devices offers a unique lens into human behavior, yet reconstructing full-body 3D motion remains difficult due to frequent self-occlusion and the 'out-of-sight' nature of the wearer's limbs. While head and hand trajectories provide sparse anchor points, current methods often o...","url_abs":"https://arxiv.org/abs/2512.19283","url_pdf":"https://arxiv.org/pdf/2512.19283v2","authors":"[\"Kyungwon Cho\",\"Hanbyul Joo\"]","published":"2025-12-22T11:26:41Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}