Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering
This addresses the problem of 3D pose estimation for applications like motion capture and virtual reality, but it appears incremental as it builds on existing self-supervised and rendering techniques.
The paper tackles 3D human pose estimation from 2D video by proposing a self-supervised method that reconstructs video frames using a differentiable rendering pipeline, eliminating the need for manual annotations.
Inferring 3D human pose from 2D images is a challenging and long-standing problem in the field of computer vision with many applications including motion capture, virtual reality, surveillance or gait analysis for sports and medicine. We present preliminary results for a method to estimate 3D pose from 2D video containing a single person and a static background without the need for any manual landmark annotations. We achieve this by formulating a simple yet effective self-supervision task: our model is required to reconstruct a random frame of a video given a frame from another timepoint and a rendered image of a transformed human shape template. Crucially for optimisation, our ray casting based rendering pipeline is fully differentiable, enabling end to end training solely based on the reconstruction task.