  We present a new approach for synthesizing novel views of people in new
poses. Our novel differentiable renderer enables the synthesis of highly
realistic images from any viewpoint. Rather than operating over mesh-based
structures, our renderer makes use of diffuse Gaussian primitives that directly
represent the underlying skeletal structure of a human. Rendering these
primitives gives results in a high-dimensional latent image, which is then
transformed into an RGB image by a decoder network. The formulation gives rise
to a fully differentiable framework that can be trained end-to-end. We
demonstrate the effectiveness of our approach to image reconstruction on both
the Human3.6M and Panoptic Studio datasets. We show how our approach can be
used for motion transfer between individuals; novel view synthesis of
individuals captured from just a single camera; to synthesize individuals from
any virtual viewpoint; and to re-render people in novel poses. Code and video
results are available at
https://github.com/GuillaumeRochette/HumanViewSynthesis.
