  Photorealistic avatars of human faces have come a long way in recent years,
yet research along this area is limited by a lack of publicly available,
high-quality datasets covering both, dense multi-view camera captures, and rich
facial expressions of the captured subjects. In this work, we present
Multiface, a new multi-view, high-resolution human face dataset collected from
13 identities at Reality Labs Research for neural face rendering. We introduce
Mugsy, a large scale multi-camera apparatus to capture high-resolution
synchronized videos of a facial performance. The goal of Multiface is to close
the gap in accessibility to high quality data in the academic community and to
enable research in VR telepresence. Along with the release of the dataset, we
conduct ablation studies on the influence of different model architectures
toward the model's interpolation capacity of novel viewpoint and expressions.
With a conditional VAE model serving as our baseline, we found that adding
spatial bias, texture warp field, and residual connections improves performance
on novel view synthesis. Our code and data is available at:
https://github.com/facebookresearch/multiface
