Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation (2011.11534v4)

Abstract: Whole-body 3D human mesh estimation aims to reconstruct the 3D human body, hands, and face simultaneously. Although several methods have been proposed, accurate prediction of 3D hands, which consist of 3D wrist and fingers, still remains challenging due to two reasons. First, the human kinematic chain has not been carefully considered when predicting the 3D wrists. Second, previous works utilize body features for the 3D fingers, where the body feature barely contains finger information. To resolve the limitations, we present Hand4Whole, which has two strong points over previous works. First, we design Pose2Pose, a module that utilizes joint features for 3D joint rotations. Using Pose2Pose, Hand4Whole utilizes hand MCP joint features to predict 3D wrists as MCP joints largely contribute to 3D wrist rotations in the human kinematic chain. Second, Hand4Whole discards the body feature when predicting 3D finger rotations. Our Hand4Whole is trained in an end-to-end manner and produces much better 3D hand results than previous whole-body 3D human mesh estimation methods. The codes are available here at https://github.com/mks0601/Hand4Whole_RELEASE.

• The paper presents Hand4Whole, which improves 3D hand pose estimation by addressing wrist and finger rotation challenges through targeted joint feature integration.
• The methodology employs the Pose2Pose framework to merge MCP joint details with body cues, enhancing rotational fidelity and overall mesh accuracy.
• The findings outperform previous models on benchmarks like EHF and AGORA, indicating significant potential for advanced applications in VR, HCI, and biomechanics.

Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation

The paper "Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation" introduces Hand4Whole, an innovative system targeting the simultaneous reconstruction of the 3D human body, hands, and face. The research primarily addresses existing challenges in estimating accurate 3D hand poses by introducing novel solutions to improve the integration of hand dynamics within whole-body estimations.

Key Contributions

The paper identifies two main limitations in current methodologies: the insufficient consideration of the human kinematic chain for 3D wrist predictions and the inappropriate use of body features in finger rotations. To overcome these challenges, Hand4Whole utilizes:

1. Pose2Pose Framework: A new module designed for the 3D joint rotation predictions by utilizing joint features rather than relying solely on body features. This approach effectively combines hand MCP joint features with the body features, yielding more precise wrist rotational dynamics.
2. Exclusion of Body Features for Finger Rotations: The system innovatively omits the body features during the prediction of 3D finger rotations, thus minimizing the noise and inaccuracies induced by unrelated body information.

These points cumulatively advance the baseline for estimating 3D hand dynamics within the whole-body 3D mesh estimation systems and facilitate a more integrated and anatomically plausible output.

Methodological Advancements

Hand4Whole is parameterized by the following modules:

• Pose2Pose: Acts as a cornerstone for the framework, leveraging joint-specific semantic information. It integrates both positional data (3D joint positions) and rotational dynamics (3D joint rotations) to predict joint mechanics with high fidelity.
• 3D Wrist Rotations: By concentrating on MCP joint contributions, Hand4Whole delivers more accurate and coherent wrist rotations, crucial for realistic hand articulation.
• End-to-End Learnability: The system maintains efficiency and accuracy through an end-to-end training regime, optimizing both 3D joint coordinates and mesh estimations simultaneously.

Evaluation and Comparative Analysis

Hand4Whole outperforms previous whole-body mesh estimation models in multiple evaluation settings:

• On benchmarks like EHF and AGORA, Hand4Whole demonstrates superior accuracy in both MPVPE and PA metrics, particularly excelling in hand pose estimation.
• Comparisons with existing systems such as ExPose, FrankMocap, and PIXIE underscore Hand4Whole’s improved performance in handling occluded or partially visible hands by effectively utilizing body and MCP joint data.

Implications and Future Prospects

The research delineates significant practical impacts on applications reliant on precise human pose reconstruction, such as virtual reality, human-computer interaction, and clinical biomechanics. The anatomical verisimilitude offered by Hand4Whole could be pivotal in these fields, extending the scope and accuracy of model-based human tracking applications.

Theoretical implications place a refreshed emphasis on the role of the kinematic chain in holistic human modeling, suggesting potential avenues of exploration in integrating similar principles across other body parts.

Conclusion

The innovations introduced by Hand4Whole mark a substantial enhancement in the accuracy of 3D human mesh estimation systems, particularly in hand pose estimation. The use of joint-specific features and elimination of extraneous body inputs for finger dynamics sets a new standard, providing a comprehensive framework for rendering nuanced and realistic human models within computational constraints. Future developments may build upon this foundation, potentially leading to even more refined AI models for complex human dynamics.