Learning Locally Editable Virtual Humans
This work addresses the need for locally editable virtual humans in applications like gaming and VR, representing an incremental improvement over existing methods.
The paper tackles the problem of creating fully editable and customizable neural avatars by proposing a hybrid representation combining neural fields with skinned meshes, resulting in a method that generates diverse detailed avatars and achieves better model fitting performance compared to state-of-the-art methods, as shown in quantitative and qualitative experiments.
In this paper, we propose a novel hybrid representation and end-to-end trainable network architecture to model fully editable and customizable neural avatars. At the core of our work lies a representation that combines the modeling power of neural fields with the ease of use and inherent 3D consistency of skinned meshes. To this end, we construct a trainable feature codebook to store local geometry and texture features on the vertices of a deformable body model, thus exploiting its consistent topology under articulation. This representation is then employed in a generative auto-decoder architecture that admits fitting to unseen scans and sampling of realistic avatars with varied appearances and geometries. Furthermore, our representation allows local editing by swapping local features between 3D assets. To verify our method for avatar creation and editing, we contribute a new high-quality dataset, dubbed CustomHumans, for training and evaluation. Our experiments quantitatively and qualitatively show that our method generates diverse detailed avatars and achieves better model fitting performance compared to state-of-the-art methods. Our code and dataset are available at https://custom-humans.github.io/.