10 citations · 36 across the 13 of their papers we have counts for
14 papers
Real-Time Generation of Streamable Talking Portrait Video with Reference-Guided Deep Compression VAEs
Sicheng Xu, Yu Deng, Shoukang Hu +5
Video diffusion models have significantly advanced portrait video generation, yet their high computational demands limit their use in interactive applications. This work presents a…
Vid-CamEdit: Video Camera Trajectory Editing with Generative Rendering from Estimated Geometry
Junyoung Seo, Jisang Han, Jaewoo Jung +9
We introduce Vid-CamEdit, a novel framework for video camera trajectory editing, enabling the re-synthesis of monocular videos along user-defined camera paths. This task is challen…
Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency
Tianqi Liu, Zihao Huang, Zhaoxi Chen +7
We present Free4D, a novel tuning-free framework for 4D scene generation from a single image. Existing methods either focus on object-level generation, making scene-level generatio…
HumanGif: Single-View Human Diffusion with Generative Prior
Shoukang Hu, Takuya Narihira, Kazumi Fukuda +3
Previous 3D human creation methods have made significant progress in synthesizing view-consistent and temporally aligned results from sparse-view images or monocular videos. Howeve…
WildAvatar: Learning In-the-wild 3D Avatars from the Web
Zihao Huang, Shoukang Hu, Guangcong Wang +5
Existing research on avatar creation is typically limited to laboratory datasets, which require high costs against scalability and exhibit insufficient representation of the real w…
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping
Junyoung Seo, Kazumi Fukuda, Takashi Shibuya +6
Generating novel views from a single image remains a challenging task due to the complexity of 3D scenes and the limited diversity in the existing multi-view datasets to train a mo…
MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo
Tianqi Liu, Guangcong Wang, Shoukang Hu +6
We present MVSGaussian, a new generalizable 3D Gaussian representation approach derived from Multi-View Stereo (MVS) that can efficiently reconstruct unseen scenes. Specifically, 1…
GSTalker: Real-time Audio-Driven Talking Face Generation via Deformable Gaussian Splatting
Bo Chen, Shoukang Hu, Qi Chen +4
We present GStalker, a 3D audio-driven talking face generation model with Gaussian Splatting for both fast training (40 minutes) and real-time rendering (125 FPS) with a 35 m…
GauHuman: Articulated Gaussian Splatting from Monocular Human Videos
Shoukang Hu, Ziwei Liu
We present, GauHuman, a 3D human model with Gaussian Splatting for both fast training (1 ~ 2 minutes) and real-time rendering (up to 189 FPS), compared with existing NeRF-based imp…
HumanLiff: Layer-wise 3D Human Generation with Diffusion Model
Shoukang Hu, Fangzhou Hong, Tao Hu +5
3D human generation from 2D images has achieved remarkable progress through the synergistic utilization of neural rendering and generative models. Existing 3D human generative mode…
ConsistentNeRF: Enhancing Neural Radiance Fields with 3D Consistency for Sparse View Synthesis
Shoukang Hu, Kaichen Zhou, Kaiyu Li +6
Neural Radiance Fields (NeRF) has demonstrated remarkable 3D reconstruction capabilities with dense view images. However, its performance significantly deteriorates under sparse vi…
SHERF: Generalizable Human NeRF from a Single Image
Shoukang Hu, Fangzhou Hong, Liang Pan +3
Existing Human NeRF methods for reconstructing 3D humans typically rely on multiple 2D images from multi-view cameras or monocular videos captured from fixed camera views. However,…
Generalizing Few-Shot NAS with Gradient Matching
Shoukang Hu, Ruochen Wang, Lanqing Hong +3
Efficient performance estimation of architectures drawn from large search spaces is essential to Neural Architecture Search. One-Shot methods tackle this challenge by training one…
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
Kaichen Zhou, Lanqing Hong, Shoukang Hu +4
Automated machine learning (AutoML) usually involves several crucial components, such as Data Augmentation (DA) policy, Hyper-Parameter Optimization (HPO), and Neural Architecture…