Mirror-Aware Neural Humans

Human motion capture either requires multi-camera systems or is unreliable when using single-view input due to depth ambiguities. Meanwhile, mirrors are readily available in urban environments and form an affordable alternative by recording two views with only a single camera. However, the mirror se...

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Bibliographic Details
Published in:arXiv.org 2024-05
Main Authors: Ajisafe, Daniel, Tang, James, Shih-Yang, Su, Wandt, Bastian, Rhodin, Helge
Format: Article
Language:English
Subjects:
Cameras
Estimation
Human motion
Learning
Motion capture
Occlusion
Pose estimation
Three dimensional motion
Urban environments
Online Access:Get full text
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Summary:Human motion capture either requires multi-camera systems or is unreliable when using single-view input due to depth ambiguities. Meanwhile, mirrors are readily available in urban environments and form an affordable alternative by recording two views with only a single camera. However, the mirror setting poses the additional challenge of handling occlusions of real and mirror image. Going beyond existing mirror approaches for 3D human pose estimation, we utilize mirrors for learning a complete body model, including shape and dense appearance. Our main contributions are extending articulated neural radiance fields to include a notion of a mirror, making it sample-efficient over potential occlusion regions. Together, our contributions realize a consumer-level 3D motion capture system that starts from off-the-shelf 2D poses by automatically calibrating the camera, estimating mirror orientation, and subsequently lifting 2D keypoint detections to 3D skeleton pose that is used to condition the mirror-aware NeRF. We empirically demonstrate the benefit of learning a body model and accounting for occlusion in challenging mirror scenes.
ISSN:2331-8422