  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.
