69 citations · 116 across the 17 of their papers we have counts for
6 papers · 1 filter
InvertAvatar: Incremental GAN Inversion for Generalized Head Avatars
Xiaochen Zhao, Jingxiang Sun, Lizhen Wang +2
While high fidelity and efficiency are central to the creation of digital head avatars, recent methods relying on 2D or 3D generative models often experience limitations such as sh…
High-Fidelity 3D Head Avatars Reconstruction through Spatially-Varying Expression Conditioned Neural Radiance Field
Minghan Qin, Yifan Liu, Yuelang Xu +3
One crucial aspect of 3D head avatar reconstruction lies in the details of facial expressions. Although recent NeRF-based photo-realistic 3D head avatar methods achieve high-qualit…
HAvatar: High-fidelity Head Avatar via Facial Model Conditioned Neural Radiance Field
Xiaochen Zhao, Lizhen Wang, Jingxiang Sun +3
The problem of modeling an animatable 3D human head avatar under light-weight setups is of significant importance but has not been well solved. Existing 3D representations either p…
AvatarReX: Real-time Expressive Full-body Avatars
Zerong Zheng, Xiaochen Zhao, Hongwen Zhang +2
We present AvatarReX, a new method for learning NeRF-based full-body avatars from video data. The learnt avatar not only provides expressive control of the body, hands and the face…
LatentAvatar: Learning Latent Expression Code for Expressive Neural Head Avatar
Yuelang Xu, Hongwen Zhang, Lizhen Wang +4
Existing approaches to animatable NeRF-based head avatars are either built upon face templates or use the expression coefficients of templates as the driving signal. Despite the pr…
StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video
Lizhen Wang, Xiaochen Zhao, Jingxiang Sun +4
Face reenactment methods attempt to restore and re-animate portrait videos as realistically as possible. Existing methods face a dilemma in quality versus controllability: 2D GAN-b…