BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos
This addresses the need for efficient and robust motion capture in computer vision and animation, offering a novel approach that reduces computational burden and tuning requirements.
The paper tackles the problem of inefficient and complex markerless motion capture from sparse multiview videos by introducing BundleMoCap, which solves it in a single stage without temporal smoothness objectives, outperforming state-of-the-art methods while maintaining simplicity.
Capturing smooth motions from videos using markerless techniques typically involves complex processes such as temporal constraints, multiple stages with data-driven regression and optimization, and bundle solving over temporal windows. These processes can be inefficient and require tuning multiple objectives across stages. In contrast, BundleMoCap introduces a novel and efficient approach to this problem. It solves the motion capture task in a single stage, eliminating the need for temporal smoothness objectives while still delivering smooth motions. BundleMoCap outperforms the state-of-the-art without increasing complexity. The key concept behind BundleMoCap is manifold interpolation between latent keyframes. By relying on a local manifold smoothness assumption, we can efficiently solve a bundle of frames using a single code. Additionally, the method can be implemented as a sliding window optimization and requires only the first frame to be properly initialized, reducing the overall computational burden. BundleMoCap's strength lies in its ability to achieve high-quality motion capture results with simplicity and efficiency. More details can be found at https://moverseai.github.io/bundle/.