Dyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Camera (2412.12861v3)

Abstract: We propose Dyn-HaMR, to the best of our knowledge, the first approach to reconstruct 4D global hand motion from monocular videos recorded by dynamic cameras in the wild. Reconstructing accurate 3D hand meshes from monocular videos is a crucial task for understanding human behaviour, with significant applications in augmented and virtual reality (AR/VR). However, existing methods for monocular hand reconstruction typically rely on a weak perspective camera model, which simulates hand motion within a limited camera frustum. As a result, these approaches struggle to recover the full 3D global trajectory and often produce noisy or incorrect depth estimations, particularly when the video is captured by dynamic or moving cameras, which is common in egocentric scenarios. Our Dyn-HaMR consists of a multi-stage, multi-objective optimization pipeline, that factors in (i) simultaneous localization and mapping (SLAM) to robustly estimate relative camera motion, (ii) an interacting-hand prior for generative infilling and to refine the interaction dynamics, ensuring plausible recovery under (self-)occlusions, and (iii) hierarchical initialization through a combination of state-of-the-art hand tracking methods. Through extensive evaluations on both in-the-wild and indoor datasets, we show that our approach significantly outperforms state-of-the-art methods in terms of 4D global mesh recovery. This establishes a new benchmark for hand motion reconstruction from monocular video with moving cameras. Our project page is at https://dyn-hamr.github.io/.

• The paper introduces a multi-stage optimization framework that disentangles hand motions from camera movements, achieving lower error rates and smoother trajectories.
• It employs generative motion infilling and an interacting-hand prior to robustly reconstruct occluded and rapid hand movements.
• The approach enhances AR/VR applications and paves the way for improved human-computer interaction, including sign language translation.

An Overview of Dyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Camera

The paper presents Dyn-HaMR, an innovative approach designed to reconstruct 4D global hand motion from monocular videos captured by dynamic cameras in real environments. This research addresses a significant challenge in computer vision and human-computer interaction: recovering accurate 3D hand trajectories amidst complex interactions and motion-induced confounders, such as camera movement. The primary focus lies on advancing augmented and virtual reality applications by improving the understanding and capture of human hand dynamics, especially when traditional assumptions, like static cameras, are violated.

Technical Contributions

1. Multi-Stage Optimization Framework: The core of Dyn-HaMR is a nuanced optimization pipeline that effectively disentangles hand motions from camera movements. This involves:
   • Simultaneous Localization and Mapping (SLAM): Employed for estimating the camera's relative motion, crucial for understanding context and movement trajectories.
   • Interacting-Hand Prior: Utilized for generative infilling and refining dynamic interactions amidst occlusions, improving robustness to missing data.
   • Hierarchical Initialization: Combines state-of-the-art methods for hand tracking to establish a robust starting point for further iterations.
2. Generative Motion Infilling: The framework leverages learned motion priors to intelligently "fill the gaps" in sequences where occlusions or rapid movements lead to missing data. This step is critical for maintaining continuity and realism in hand motion reconstruction.
3. Handling Dynamic Cameras: Through the incorporation of SLAM and a learnable scale factor, Dyn-HaMR resolves ambiguities between camera and hand motions, achieving more accurate global hand poses and trajectories.

Numerical Results and Evaluation

The paper reports extensive experiments demonstrating Dyn-HaMR's superior performance over existing methods. Evaluations are conducted on both in-the-wild datasets and controlled indoor environments. The results indicate that Dyn-HaMR achieves lower Mean Per Joint Error (MPJPE) and reduces acceleration errors, underlining its capability to produce smoother and more physically plausible hand motions.

Implications and Future Work

Dyn-HaMR represents a significant advancement in handling dynamic visual data for interaction capture. This technology not only enhances virtual reality but also has the potential to improve fields like sign language translation, where high fidelity motion capture is crucial.

The paper suggests several avenues for future research:

• Extended Sequence Processing: While Dyn-HaMR can reconstruct 128-frame sequences efficiently, extending its capabilities to process longer sequences without compromising accuracy is a valuable direction.
• Augmented Hand Priors: Improving the interacting-hand motion priors, possibly integrating object interactions more robustly, would enhance the system's comprehensiveness and usability.

In conclusion, Dyn-HaMR offers a robust solution to one of the pressing challenges in motion capture technology, paving the way for more dynamic and interactive computer-mediated environments.