HART: Human Aligned Reconstruction Transformer

• URL: http://arxiv.org/abs/2509.26621v1
• Date: Tue, 30 Sep 2025 17:56:02 GMT
• Title: HART: Human Aligned Reconstruction Transformer
• Authors: Xiyi Chen, Shaofei Wang, Marko Mihajlovic, Taewon Kang, Sergey Prokudin, Ming Lin,
• Abstract summary: HART is a unified framework for sparse-view human reconstruction.<n>It outputs a watertight clothed mesh, the aligned SMPL-X body mesh, and a Gaussian-splat representation for novel-view rendering.<n>Results suggest that feed-forward transformers can serve as a scalable model for robust human reconstruction in real-world settings.
• Score: 17.065147884544853
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce HART, a unified framework for sparse-view human reconstruction. Given a small set of uncalibrated RGB images of a person as input, it outputs a watertight clothed mesh, the aligned SMPL-X body mesh, and a Gaussian-splat representation for photorealistic novel-view rendering. Prior methods for clothed human reconstruction either optimize parametric templates, which overlook loose garments and human-object interactions, or train implicit functions under simplified camera assumptions, limiting applicability in real scenes. In contrast, HART predicts per-pixel 3D point maps, normals, and body correspondences, and employs an occlusion-aware Poisson reconstruction to recover complete geometry, even in self-occluded regions. These predictions also align with a parametric SMPL-X body model, ensuring that reconstructed geometry remains consistent with human structure while capturing loose clothing and interactions. These human-aligned meshes initialize Gaussian splats to further enable sparse-view rendering. While trained on only 2.3K synthetic scans, HART achieves state-of-the-art results: Chamfer Distance improves by 18-23 percent for clothed-mesh reconstruction, PA-V2V drops by 6-27 percent for SMPL-X estimation, LPIPS decreases by 15-27 percent for novel-view synthesis on a wide range of datasets. These results suggest that feed-forward transformers can serve as a scalable model for robust human reconstruction in real-world settings. Code and models will be released.

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