HandOS: 3D Hand Reconstruction in One Stage

2024-12-02

arXiv.org

AI Summary

Existing hand reconstruction methods typically adopt a multi-stage paradigm—detection → left/right classification → pose estimation—leading to computational redundancy and error propagation. This paper proposes HandOS, the first end-to-end, single-stage 3D hand reconstruction framework that jointly performs hand detection, 2D keypoint localization, and 3D mesh generation. Its core contributions are: (1) a lightweight end-to-end architecture leveraging a frozen off-the-shelf object detector; (2) a novel interactive 2D–3D decoder that implicitly encodes hand laterality without explicit classification; and (3) a hierarchical attention mechanism jointly optimizing 2D joint locations, 3D mesh vertices, and camera translation parameters. Evaluated on FreiHand, HandOS achieves 5.0 mm PA-MPJPE; on HInt-Ego4D, it attains 64.6% PCK@0.05—setting new state-of-the-art performance.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Learning with Manifolds

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Existing approaches of hand reconstruction predominantly adhere to a multi-stage framework, encompassing detection, left-right classification, and pose estimation. This paradigm induces redundant computation and cumulative errors. In this work, we propose HandOS, an end-to-end framework for 3D hand reconstruction. Our central motivation lies in leveraging a frozen detector as the foundation while incorporating auxiliary modules for 2D and 3D keypoint estimation. In this manner, we integrate the pose estimation capacity into the detection framework, while at the same time obviating the necessity of using the left-right category as a prerequisite. Specifically, we propose an interactive 2D-3D decoder, where 2D joint semantics is derived from detection cues while 3D representation is lifted from those of 2D joints. Furthermore, hierarchical attention is designed to enable the concurrent modeling of 2D joints, 3D vertices, and camera translation. Consequently, we achieve an end-to-end integration of hand detection, 2D pose estimation, and 3D mesh reconstruction within a one-stage framework, so that the above multi-stage drawbacks are overcome. Meanwhile, the HandOS reaches state-of-the-art performances on public benchmarks, e.g., 5.0 PA-MPJPE on FreiHand and 64.6% PCK@0.05 on HInt-Ego4D. Project page: idea-research.github.io/HandOSweb.

Problem

• Eliminates multi-stage hand reconstruction inefficiencies
• Integrates 2D and 3D keypoint estimation in one framework
• Overcomes cumulative errors in hand pose estimation

Innovation

• End-to-end 3D hand reconstruction framework
• Interactive 2D-3D decoder for joint estimation
• Hierarchical attention for concurrent 2D-3D modeling