Head Avatar
Head avatar research focuses on creating realistic, animatable 3D models of human heads for applications in virtual and augmented reality, gaming, and film. Current efforts concentrate on developing efficient and high-fidelity models using various architectures, including neural radiance fields (NeRFs), 3D Gaussian splatting, and parametric models like 3D Morphable Models (3DMMs), often incorporating techniques like diffusion models and GANs for enhanced realism and control. These advancements are significant because they enable more immersive and interactive experiences in various digital environments, pushing the boundaries of realistic human representation in computer graphics.
Papers
MonoGaussianAvatar: Monocular Gaussian Point-based Head Avatar
Yufan Chen, Lizhen Wang, Qijing Li, Hongjiang Xiao, Shengping Zhang, Hongxun Yao, Yebin Liu
SingingHead: A Large-scale 4D Dataset for Singing Head Animation
Sijing Wu, Yunhao Li, Weitian Zhang, Jun Jia, Yucheng Zhu, Yichao Yan, Guangtao Zhai
HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
Helisa Dhamo, Yinyu Nie, Arthur Moreau, Jifei Song, Richard Shaw, Yiren Zhou, Eduardo Pérez-Pellitero
Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians
Yuelang Xu, Benwang Chen, Zhe Li, Hongwen Zhang, Lizhen Wang, Zerong Zheng, Yebin Liu
GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians
Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, Matthias Nießner
GaussianHead: High-fidelity Head Avatars with Learnable Gaussian Derivation
Jie Wang, Jiu-Cheng Xie, Xianyan Li, Feng Xu, Chi-Man Pun, Hao Gao