We propose a parametric model that maps free-view images into a vector space of coded facial shape, expression and appearance using a neural radiance field, namely Morphable Facial NeRF. MoFaNeRF can be used to synthesize free-view images by fitting to a single image or generating from a random code. The synthesized face is morphable that can be rigged to any expressions and be edited to any shapes or appearances.
ECCV 2022 Paper    Code    Project Page

This paper presents a novel method to recover detailed avatar from a single image. A learning-based framework is proposed to combine the robustness of the parametric model with the flexibility of free-form 3D deformation. The neural networks are used to refine the 3D shape in a Hierarchical Mesh Deformation (HMD) framework, and restore detailed human body shapes with complete textures beyond skinned models.
TPAMI 2021 paper

We explore a novel direction to apply the learningbased framework, consisting of a pre-processing module for point cloud voxelization, scaling and partition, compression network for rate-distortion optimized representation, and a post-processing module for point cloud
reconstruction and rendering, to represent point clouds geometry using compact features with the state-of-the-art compression efficiency.
TCSVT 2021 Paper    Code

Author's picture

Hao Zhu

CITE Lab - 3DV Group, Nanjing University
E-mail: zhuhaoese@nju.edu.cn

Associate Researcher

Nanjing, China