AHAP: Reconstructing Arbitrary Humans from Arbitrary Perspectives with Geometric Priors

• URL: http://arxiv.org/abs/2602.23951v1
• Date: Fri, 27 Feb 2026 11:53:45 GMT
• Title: AHAP: Reconstructing Arbitrary Humans from Arbitrary Perspectives with Geometric Priors
• Authors: Xiaozhen Qiao, Wenjia Wang, Zhiyuan Zhao, Jiacheng Sun, Ping Luo, Hongyuan Zhang, Xuelong Li,
• Abstract summary: We present textbfAHAP, a feed-forward framework for reconstructing arbitrary humans from arbitrary camera perspectives.<n>Our core lies in the effective fusion of multi-view geometry to assist human association, reconstruction and localization.<n>A Human Head fuses cross-view features and scene context for SMPL prediction, guided by cross-view reprojection losses to enforce body pose consistency.
• Score: 81.50960055126156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstructing 3D humans from images captured at multiple perspectives typically requires pre-calibration, like using checkerboards or MVS algorithms, which limits scalability and applicability in diverse real-world scenarios. In this work, we present \textbf{AHAP} (Reconstructing \textbf{A}rbitrary \textbf{H}umans from \textbf{A}rbitrary \textbf{P}erspectives), a feed-forward framework for reconstructing arbitrary humans from arbitrary camera perspectives without requiring camera calibration. Our core lies in the effective fusion of multi-view geometry to assist human association, reconstruction and localization. Specifically, we use a Cross-View Identity Association module through learnable person queries and soft assignment, supervised by contrastive learning to resolve cross-view human identity association. A Human Head fuses cross-view features and scene context for SMPL prediction, guided by cross-view reprojection losses to enforce body pose consistency. Additionally, multi-view geometry eliminates the depth ambiguity inherent in monocular methods, providing more precise 3D human localization through multi-view triangulation. Experiments on EgoHumans and EgoExo4D demonstrate that AHAP achieves competitive performance on both world-space human reconstruction and camera pose estimation, while being 180$\times$ faster than optimization-based approaches.

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