Learning One-Shot 4D Head Avatar Synthesis using Synthetic Data
Yu Deng Duomin Wang Xiaohang Ren Xingyu Chen Baoyuan Wang
Existing one-shot 4D head synthesis methods usually learn from monocular
videos with the aid of 3DMM reconstruction, yet the latter is evenly
challenging which restricts them from reasonable 4D head synthesis. We present
a method to learn one-shot 4D head synthesis via large-scale synthetic data.
The key is to first learn a part-wise 4D generative model from monocular images
via adversarial learning, to synthesize multi-view images of diverse identities
and full motions as training data; then leverage a transformer-based animatable
triplane reconstructor to learn 4D head reconstruction using the synthetic
data. A novel learning strategy is enforced to enhance the generalizability to
real images by disentangling the learning process of 3D reconstruction and
reenactment. Experiments demonstrate our superiority over the prior art.