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

We propose a very convenient system of scanning a human body using only a conventional video camera without the aid of special sensor or controlled illumination. The point cloud reinforcement is proposed to detect and adjust the conflict point data for the slender and shaky body part, which achieves reasonable and plausible mesh reconstruction.
TCSVT 2017 paper    Project Page

Hi3D system automatically reconstructs high-quality 3D mesh model using single camera and a turntable. The system aims at modeling small-scale still object with low-cost hardware. Several refining algorithms are involved to make the end-to-end system robust and accurate.
Project Page

Author's picture

Hao Zhu

CITE Lab - 3DV Group, Nanjing University

Associate Researcher

Nanjing, China