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{SMPLpix}: Neural Avatars from {3D} Human Models


Conference Paper


Recent advances in deep generative models have led to an unprecedented level of realism for synthetically generated images of humans. However, one of the remaining fundamental limitations of these models is the ability to flexibly control the generative process, e.g. change the camera and human pose while retaining the subject identity. At the same time, deformable human body models like SMPL \cite{loper2015smpl} and its successors provide full control over pose and shape, but rely on classic computer graphics pipelines for rendering. Such rendering pipelines require explicit mesh rasterization that (a) does not have the potential to fix artifacts or lack of realism in the original 3D geometry and (b) until recently, were not fully incorporated into deep learning frameworks. In this work, we propose to bridge the gap between classic geometry-based rendering and the latest generative networks operating in pixel space. We train a network that directly converts a sparse set of 3D mesh vertices into photorealistic images, alleviating the need for traditional rasterization mechanism. We train our model on a large corpus of human 3D models and corresponding real photos, and show the advantage over conventional differentiable renderers both in terms of the level of photorealism and rendering efficiency.

Author(s): Sergey Prokudin and Michael J. Black and Javier Romero
Book Title: Winter Conference on Applications of Computer Vision (WACV)
Year: 2021
Month: January

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Links: project


  title = {{SMPLpix}: Neural Avatars from {3D} Human Models},
  author = {Prokudin, Sergey and Black, Michael J. and Romero, Javier},
  booktitle = {Winter Conference on Applications of Computer Vision (WACV)},
  month = jan,
  year = {2021},
  month_numeric = {1}