69 citations · 116 across the 17 of their papers we have counts for
17 papers
GeoDiff4D: Geometry-Aware Diffusion for 4D Head Avatar Reconstruction
Chao Xu, Xiaochen Zhao, Xiang Deng +4
Reconstructing photorealistic and animatable 4D head avatars from a single portrait image remains a fundamental challenge in computer vision. While diffusion models have enabled re…
Plan-X: Instruct Video Generation via Semantic Planning
Lun Huang, You Xie, Hongyi Xu +7
Diffusion Transformers have demonstrated remarkable capabilities in visual synthesis, yet they often struggle with high-level semantic reasoning and long-horizon planning. This lim…
X-Streamer: Unified Human World Modeling with Audiovisual Interaction
You Xie, Tianpei Gu, Zenan Li +7
We introduce X-Streamer, an end-to-end multimodal human world modeling framework for building digital human agents capable of infinite interactions across text, speech, and video w…
X-UniMotion: Animating Human Images with Expressive, Unified and Identity-Agnostic Motion Latents
Guoxian Song, Hongyi Xu, Xiaochen Zhao +5
We present X-UniMotion, a unified and expressive implicit latent representation for whole-body human motion, encompassing facial expressions, body poses, and hand gestures. Unlike…
X-Actor: Emotional and Expressive Long-Range Portrait Acting from Audio
Chenxu Zhang, Zenan Li, Hongyi Xu +8
We present X-Actor, a novel audio-driven portrait animation framework that generates lifelike, emotionally expressive talking head videos from a single reference image and an input…
X-NeMo: Expressive Neural Motion Reenactment via Disentangled Latent Attention
Xiaochen Zhao, Hongyi Xu, Guoxian Song +6
We propose X-NeMo, a novel zero-shot diffusion-based portrait animation pipeline that animates a static portrait using facial movements from a driving video of a different individu…
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…
DreamCraft3D++: Efficient Hierarchical 3D Generation with Multi-Plane Reconstruction Model
Jingxiang Sun, Cheng Peng, Ruizhi Shao +6
We introduce DreamCraft3D++, an extension of DreamCraft3D that enables efficient high-quality generation of complex 3D assets. DreamCraft3D++ inherits the multi-stage generation pr…
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…
High-Fidelity 3D Head Avatars Reconstruction through Spatially-Varying Expression Conditioned Neural Radiance Field
Minghan Qin, Yifan Liu, Yuelang Xu +3
One crucial aspect of 3D head avatar reconstruction lies in the details of facial expressions. Although recent NeRF-based photo-realistic 3D head avatar methods achieve high-qualit…
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…
AvatarReX: Real-time Expressive Full-body Avatars
Zerong Zheng, Xiaochen Zhao, Hongwen Zhang +2
We present AvatarReX, a new method for learning NeRF-based full-body avatars from video data. The learnt avatar not only provides expressive control of the body, hands and the face…
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…
Vehicle Reconstruction and Texture Estimation Using Deep Implicit Semantic Template Mapping
Xiaochen Zhao, Zerong Zheng, Chaonan Ji +5
We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments. To fully utili…
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…