: Random examples of people generated with our model. For each row, sampling is conditioned on the silhouette displayed on the left. Our proposed framework also supports unconditioned sampling as well as conditioning on local appearance cues, such as color. We present the first image-based generative model of people in clothing for the full body. We sidestep the commonly used complex graphics rendering pipeline and the need for high-quality 3D scans of dressed people. Instead, we learn generative models from a large image database. The main challenge is to cope with the high variance in human pose, shape and appearance. For this reason, pure image-based approaches have not been considered so far. We show that this challenge can be overcome by splitting the generating process in two parts. First, we learn to generate a semantic segmentation of the body and clothing. Second, we learn a conditional model on the resulting segments that creates realistic images. The full model is differentiable and can be conditioned on pose, shape or color. The result are samples of people in different clothing items and styles. The proposed model can generate entirely new people with realistic clothing. In several experiments we present encouraging results that suggest an entirely data-driven approach to people generation is possible. * This work was performed while P. V. Gehler was with the BCCN 1 and MPI-IS 2 .
E.g. REF presents a method to produce full-body images of people in clothing from semantic segmentation of the body and clothing.
A Generative Model of People in Clothing
2017 IEEE International Conference on Computer Vision (ICCV)
2017 IEEE International Conference on Computer Vision (ICCV)
Computer Science