Problem
Research questions and friction points this paper is trying to address.
3D reconstruction
deep learning limitations
human model
Human as Points: Explicit Point-based 3D Human Reconstruction from Single-view RGB Images
🤖 AI Summary
Existing implicit-representation-based methods for 3D human reconstruction from a single RGB image suffer from limited generalization, robustness, and geometric controllability. This paper proposes the first fully explicit, point-cloud-driven end-to-end framework—eliminating implicit functions entirely and directly regressing, generating, and optimizing human point clouds in 3D space. Key contributions include: (1) a geometric center regression paradigm to enhance structural controllability; (2) an SMPL-guided explicit point cloud estimation and refinement network; and (3) a prior-constrained point cloud generation and registration mechanism. Our method achieves significant improvements over state-of-the-art approaches across multiple benchmarks, with 20–40% reductions in Chamfer Distance (CD) and Mean Per-Joint Position Error (MPJPE), yielding more detailed and complete reconstructions. Code and data are publicly released.
📝 Abstract
The latest trends in the research field of single-view human reconstruction devote to learning deep implicit functions constrained by explicit body shape priors. Despite the remarkable performance improvements compared with traditional processing pipelines, existing learning approaches still show different aspects of limitations in terms of flexibility, generalizability, robustness, and/or representation capability. To comprehensively address the above issues, in this paper, we investigate an explicit point-based human reconstruction framework called HaP, which adopts point clouds as the intermediate representation of the target geometric structure. Technically, our approach is featured by fully-explicit point cloud estimation, manipulation, generation, and refinement in the 3D geometric space, instead of an implicit learning process that can be ambiguous and less controllable. The overall workflow is carefully organized with dedicated designs of the corresponding specialized learning components as well as processing procedures. Extensive experiments demonstrate that our framework achieves quantitative performance improvements of 20% to 40% over current state-of-the-art methods, and better qualitative results. Our promising results may indicate a paradigm rollback to the fully-explicit and geometry-centric algorithm design, which enables to exploit various powerful point cloud modeling architectures and processing techniques. We will make our code and data publicly available at https://github.com/yztang4/HaP.
Innovation
Methods, ideas, or system contributions that make the work stand out.
HaP Framework
Point Cloud Technology
3D Human Model Reconstruction
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