Optical Flow-based 3D Human Motion Estimation from Monocular Video
This addresses the challenge of 3D human motion estimation from monocular video, which is important for applications like animation and robotics, but the approach appears incremental as it builds on existing flow-based regularization techniques.
The paper tackled the problem of estimating 3D human motion and body shape from monocular video by using optical flow to constrain poses, resulting in a method that requires only a single initialization step and demonstrates effective regularization for this under-constrained task.
We present a generative method to estimate 3D human motion and body shape from monocular video. Under the assumption that starting from an initial pose optical flow constrains subsequent human motion, we exploit flow to find temporally coherent human poses of a motion sequence. We estimate human motion by minimizing the difference between computed flow fields and the output of an artificial flow renderer. A single initialization step is required to estimate motion over multiple frames. Several regularization functions enhance robustness over time. Our test scenarios demonstrate that optical flow effectively regularizes the under-constrained problem of human shape and motion estimation from monocular video.