69 citations · 165 across the 16 of their papers we have counts for
16 papers
Stereo-Talker: Audio-driven 3D Human Synthesis with Prior-Guided Mixture-of-Experts
Xiang Deng, Youxin Pang, Xiaochen Zhao +6
This paper introduces Stereo-Talker, a novel one-shot audio-driven human video synthesis system that generates 3D talking videos with precise lip synchronization, expressive body g…
GPHM: Gaussian Parametric Head Model for Monocular Head Avatar Reconstruction
Yuelang Xu, Zhaoqi Su, Qingyao Wu +1
Creating high-fidelity 3D human head avatars is crucial for applications in VR/AR, digital human, and film production. Recent advances have leveraged morphable face models to gener…
MonoGaussianAvatar: Monocular Gaussian Point-based Head Avatar
Yufan Chen, Lizhen Wang, Qijing Li +4
The ability to animate photo-realistic head avatars reconstructed from monocular portrait video sequences represents a crucial step in bridging the gap between the virtual and real…
GMTalker: Gaussian Mixture-based Audio-Driven Emotional Talking Video Portraits
Yibo Xia, Lizhen Wang, Xiang Deng +3
Synthesizing high-fidelity and emotion-controllable talking video portraits, with audio-lip sync, vivid expressions, realistic head poses, and eye blinks, has been an important and…
HHAvatar: Gaussian Head Avatar with Dynamic Hairs
Zhanfeng Liao, Yuelang Xu, Zhe Li +6
Creating high-fidelity 3D head avatars has always been a research hotspot, but it remains a great challenge under lightweight sparse view setups. In this paper, we propose HHAvatar…
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…
Animatable and Relightable Gaussians for High-fidelity Human Avatar Modeling
Zhe Li, Yipengjing Sun, Zerong Zheng +3
Modeling animatable human avatars from RGB videos is a long-standing and challenging problem. Recent works usually adopt MLP-based neural radiance fields (NeRF) to represent 3D hum…
DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior
Jingxiang Sun, Bo Zhang, Ruizhi Shao +4
We present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image t…
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…
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…
AvatarMAV: Fast 3D Head Avatar Reconstruction Using Motion-Aware Neural Voxels
Yuelang Xu, Lizhen Wang, Xiaochen Zhao +2
With NeRF widely used for facial reenactment, recent methods can recover photo-realistic 3D head avatar from just a monocular video. Unfortunately, the training process of the NeRF…
Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars
Jingxiang Sun, Xuan Wang, Lizhen Wang +4
3D-aware generative adversarial networks (GANs) synthesize high-fidelity and multi-view-consistent facial images using only collections of single-view 2D imagery. Towards fine-grai…
IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis
Jingxiang Sun, Xuan Wang, Yichun Shi +3
Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution or high-quality ones with no editi…
FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset
Lizhen Wang, Zhiyuan Chen, Tao Yu +3
We present FaceVerse, a fine-grained 3D Neural Face Model, which is built from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K high-fidelity 3D head scan m…
NormalGAN: Learning Detailed 3D Human from a Single RGB-D Image
Lizhen Wang, Xiaochen Zhao, Tao Yu +2
We propose NormalGAN, a fast adversarial learning-based method to reconstruct the complete and detailed 3D human from a single RGB-D image. Given a single front-view RGB-D image, N…