We propose VividTalk, a two-stage generic framework that supports generating high-visual quality talking head videos with all the above properties. Extensive experiments show that the proposed VividTalk can generate high-visual quality talking head videos with lip-sync and realistic enhanced by a large margin.
3DV 2025    Project Page

We present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input.
TPAMI 2024    Code & Dataset

We propose a novel method, AvatarBooth, for generating high-quality 3D avatars from text prompts or photos. Unlike previous approaches that are based on only text descriptions, our method enables the users to customize the generated avatars according to casually captured photos of the face or full body.
arXiv 2023    Project Page

We present LoD-NeuS, an efficient neural representation for high-frequency geometry detail recovery and anti-aliased novel view rendering. A multi-scale tri-plane-based scene representation is introduced to capture the LoD of the signed distance function (SDF) and the space radiance.
SIGGRAPH Aisa 2023 Conf.

We propose to synthesize high-quality 3D face models from natural language descriptions including both concrete and abstract descriptions. Describe3D dataset is established with large-scale 3D faces and fine-grained text descriptions for text-to-3D face generation task.
CVPR 2023    Project Page   Code

We propose a novel approach for the single-view 3D face reconstruction task in a non-parametric scheme. Our method gets rid of the heavy dependence on the statistic model and, therefore, its limitations and achieves state-of-the-art performance by learning from our created pseudo 2D&3D datasets.
AAAI 2023 (Oral)    Project Page    Code

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    Project Page    Code

we propose a novel framework to efficiently recover detailed facial geometry from calibrated multi-view images. The cost regression and multi-view fusion is solved by learning an implicit function in our network, which is proved to be more accurate and time-saving.
AAAI 2022 (Oral)    Code

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 2022

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    Code