Reposing Humans by Warping 3D Features

IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 4499–4504

Abstract

We address the problem of reposing an image of a human into any desired novel pose. This conditional image-generation task requires reasoning about the 3D structure of the human, including self-occluded body parts. Most prior works are either based on 2D representations or require fitting and manipulating an explicit 3D body mesh. Based on the recent success in deep learning-based volumetric representations, we propose to implicitly learn a dense feature volume from human images, which lends itself to simple and intuitive manipulation through explicit geometric warping. Once the latent feature volume is warped according to the desired pose change, the volume is mapped back to RGB space by a convolutional decoder.Our state-of-the-art results on the DeepFashion and the iPER benchmarks indicate that dense volumetric human representations are worth investigating in more detail.

Authors 3

1. Markus Knoche Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany

   RWTH Aachen University (Germany)

2. István Sárándi Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany

   RWTH Aachen University (Germany)

3. Bastian Leibe Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany

   RWTH Aachen University (Germany)