OmniHands: Towards Robust 4D Hand Mesh Recovery via A Versatile Transformer
Summary: arXiv:2405.20330v4 Announce Type: replace-cross
Abstract
In this paper, we introduce OmniHands, a universal approach to recovering interactive hand meshes and their relative movement from monocular or multi-view inputs. Our approach addresses two major limitations of previous methods: lacking a unified solution for handling various hand image inputs and neglecting the positional relationship of two hands within images.
Introduction
The ability to accurately capture and reconstruct hand movements is crucial for numerous applications, including virtual reality, gaming, and human-computer interaction. Traditional methods have struggled to provide a comprehensive solution that accommodates different types of image inputs while also considering the spatial relationships between hands. OmniHands aims to bridge this gap by introducing innovative techniques in hand mesh recovery.
Key Features of OmniHands
Our approach is built on two fundamental advancements:
- Relation-aware Two-Hand Tokenization (RAT): This method embeds positional relation information into hand tokens. By doing so, our network effectively manages both single-hand and two-hand inputs and leverages the relative positions of hands. This capability is essential for accurately reconstructing complex hand interactions observed in real-world scenarios.
- 4D Interaction Reasoning (FIR) Module: This module is designed to fuse hand tokens in 4D using attention mechanisms, ultimately decoding them into 3D hand meshes and their relative temporal movements. The integration of this module enhances the network’s ability to reason about interactive hand gestures over time.
Methodology
Our methodology involves the development of a universal architecture that employs novel tokenization and contextual feature fusion strategies. The RAT method allows for the explicit embedding of relational data between hands, which significantly improves feature fusion. The FIR module further enhances this process by enabling the network to interpret interactions in four dimensions, thus providing a more dynamic reconstruction of hand movements.
Results and Validation
The efficacy of the OmniHands approach has been validated on multiple benchmark datasets. Results from in-the-wild videos and real-world scenarios demonstrate its superior performance in interactive hand reconstruction tasks. Our evaluations reveal that OmniHands not only outperforms existing methods but also showcases enhanced adaptability across various hand movement scenarios.
Conclusion
OmniHands represents a significant advancement in the field of hand mesh recovery. By addressing the dual challenges of input variability and hand positional relationships, our approach offers a robust solution suitable for a wide range of applications. The project showcases the potential of utilizing novel tokenization and reasoning strategies in achieving impressive outcomes in hand interaction analysis.
Further Information
For more video results and detailed insights into our methodology, please visit the project page: OmniHand Project Page.