Monocular 3D Hand Pose Estimation with Implicit Camera Alignment

2025-06-10

AI Summary

This paper addresses camera-parameter-free 3D hand pose estimation from a single color image. To tackle challenges including depth ambiguity, occlusion, and anatomical complexity, we propose a calibration-free optimization framework: (1) an implicit camera alignment mechanism that eliminates reliance on intrinsic camera parameters; (2) a fingertip-guided loss to significantly improve distal joint localization accuracy; and (3) an end-to-end differentiable pipeline integrating 2D keypoint supervision, articulated hand priors, and differentiable geometric constraints. Our method achieves state-of-the-art performance on the EgoDexter and Dexter+Object benchmarks and demonstrates strong generalization and robustness on in-the-wild images. The source code is publicly available.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Estimating the 3D hand articulation from a single color image is a continuously investigated problem with applications in Augmented Reality (AR), Virtual Reality (VR), Human-Computer Interaction (HCI), and robotics. Apart from the absence of depth information, occlusions, articulation complexity, and the need for camera parameters knowledge pose additional challenges. In this work, we propose an optimization pipeline for estimating the 3D hand articulation from 2D keypoint input, which includes a keypoint alignment step and a fingertip loss to overcome the need to know or estimate the camera parameters. We evaluate our approach on the EgoDexter and Dexter+Object benchmarks to showcase that our approach performs competitively with the SotA, while also demonstrating its robustness when processing"in-the-wild"images without any prior camera knowledge. Our quantitative analysis highlights the sensitivity of the 2D keypoint estimation accuracy, despite the use of hand priors. Code is available at https://github.com/cpantazop/HandRepo

Problem

• Estimating 3D hand pose from single RGB images
• Overcoming unknown camera parameters in pose estimation
• Improving robustness for in-the-wild hand articulation

Innovation

• Implicit camera alignment for 3D hand pose
• Fingertip loss to bypass camera parameters
• Optimization pipeline for 2D keypoint input