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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2512.19283 |
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| _version_ | 1866914435683057664 |
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| author | Cho, Kyungwon Joo, Hanbyul |
| author_facet | Cho, Kyungwon Joo, Hanbyul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OmniEgoCap: Camera-Agnostic Sequence-Level Egocentric Motion Reconstruction Cho, Kyungwon Joo, Hanbyul Computer Vision and Pattern Recognition 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. |
| title | OmniEgoCap: Camera-Agnostic Sequence-Level Egocentric Motion Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.19283 |