Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering

• URL: http://arxiv.org/abs/2210.04514v1
• Date: Mon, 10 Oct 2022 09:24:07 GMT
• Title: Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering
• Authors: Luca Schmidtke, Benjamin Hou, Athanasios Vlontzos, Bernhard Kainz
• Abstract summary: Inferring 3D human pose from 2D images is a challenging and long-standing problem in the field of computer vision. We present preliminary results for a method to estimate 3D pose from 2D video containing a single person.
• Score: 5.568218439349004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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