StylePeople: A Generative Model of Fullbody Human Avatars
This addresses the need for realistic digital human avatars in applications like gaming or virtual reality, though it appears incremental as it builds on existing mesh and neural texture methods.
The authors tackled the problem of creating full-body human avatars that model clothing and hair, which is challenging for mesh-based approaches, by combining parametric mesh-based body models with neural textures, enabling generation from images or videos with available code.
We propose a new type of full-body human avatars, which combines parametric mesh-based body model with a neural texture. We show that with the help of neural textures, such avatars can successfully model clothing and hair, which usually poses a problem for mesh-based approaches. We also show how these avatars can be created from multiple frames of a video using backpropagation. We then propose a generative model for such avatars that can be trained from datasets of images and videos of people. The generative model allows us to sample random avatars as well as to create dressed avatars of people from one or few images. The code for the project is available at saic-violet.github.io/style-people.