ArXiv Preprint
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.
Luca Schmidtke, Benjamin Hou, Athanasios Vlontzos, Bernhard Kainz
2022-10-10
