1 citations · 1 across the 19 of their papers we have counts for
22 papers
Demiurge: Forging Navigable 4D Worlds via Autoregressive Video Rerendering
Hanxin Zhu, Jianqi Ma, Cong Wang +10
Recent advances in video diffusion enable novel-view video synthesis from a single input video, supporting world modeling, embodied intelligence, and robotic simulation. However, e…
PhysFlow: Physics-Aware Optical Flow for Motion Controllable Video Generation
Cong Wang, Hanxin Zhu, Yonglin Tian +5
Video generation models have recently attracted substantial attention for their ability to generate visually compelling videos, yet ensuring physically consistent and plausible dyn…
EmbodiedVAE: Disentangled Video VAE for Efficient and Controllable Embodied Manipulation
Jiayi Luo, Hanxin Zhu, Chen Gao +5
Latent diffusion models (LDMs) have recently significantly advanced embodied learning in constructing powerful embodied manipulation world models. However, despite the remarkable p…
AgentHOI: Multi-Agent Reasoning for Human-Object-Interaction Video Generation via Implicit Representation Alignment
Ziyao Huang, Shunkai Li, Juan Cao +7
Recent advances in video diffusion models have spurred interest in human-object interaction (HOI) video generation, which demands fine-grained control over interaction logic beyond…
Aura: Consistent Multi-Subject Video Generation via VLM-Grounded Semantic Alignment
Zixiang Zhou, Zhentao Yu, Yifeng Ma +9
Subject-driven and multi-element video generation are central to controllable video synthesis, but existing methods still struggle to preserve identity consistency and model comple…
Goku: A Million-Scale Universal Dataset and Benchmark for Instruction-Based Video Editing
Sen Liang, Cong Wang, Zhentao Yu +8
Existing instruction-based video editing datasets commonly focus on single-task appearance editing, failing to meet the complex creative demands of real-world scenarios. To bridge…
HarmoView: Harmonizing Multi-View Constraints for Identity-Consistent Video Generation
Cong Wang, Zhentao Yu, Hongmei Wang +7
Current identity-consistent video generation methods struggle to preserve appearance fidelity under large viewpoint changes. While introducing multi-view reference input offers a n…
CP4D: Compositional Physics-aware 4D Scene Generation
Hanxin Zhu, Cong Wang, Tianyu He +4
4D generation (\textit{i.e.}, dynamic 3D generation) has recently emerged as a rapidly growing research frontier due to its powerful spatiotemporal modeling capabilities. However,…
Embody4D: A Generalist Data Engine for Embodied 4D World Modeling
Peiyan Tu, Hanxin Zhu, Jingwen Sun +6
Embodied agents require robust and comprehensive 3D spatiotemporal representations to support spatial reasoning, manipulation understanding, and downstream decision making. However…
Physics-Informed Video Generation via Mixture-of-Experts Latent Alignment
Cong Wang, Hanxin Zhu, Jiayi Luo +6
Large-scale video generation models have made remarkable progress in semantic consistency and visual quality, producing videos that are increasingly coherent and visually convincin…
SpongeBob: Sync-Aware Harmonious Audio-Visual Generative Editing
Sen Liang, Cong Wang, Fengbin Guan +6
Visual and acoustic events in the physical world are inherently coupled, yet existing video editing methods typically adopt decoupled pipelines, lacking bidirectional modality inte…
Future Forcing: Future-aware Training-free KV Cache Policy for Autoregressive Video Generation
Jiayi Luo, Qiyan Liu, Tengyang Wang +8
Autoregressive (AR) video generation has emerged as a promising paradigm for long-horizon video synthesis, where each frame is generated conditioned on previously generated tokens.…
A Signal-Language Foundation Model for Broad-Spectrum Cardiovascular Assessment from Routine Electrocardiography
Ziqing Yu, Yuhui Tao, Jiayu Huo +19
Electrocardiography (ECG) is central to cardiovascular care, but conventional AI models are often restricted to common arrhythmias and may generalize poorly across populations or c…
GTA: Advancing Image-to-3D World Generation via Geometry Then Appearance Video Diffusion
Hanxin Zhu, Cong Wang, Peiyan Tu +4
Recent developments in generative models and large-scale datasets have substantially advanced 3D world generation, facilitating a broad range of domains including spatial intellige…
OrthoPhys: Physically Plausible Video Generation with Orthogonal-View Geometry Guidance
Cong Wang, Hanxin Zhu, Xiao Tang +4
Recent progress in video generation has led to substantial improvements in visual fidelity, yet ensuring physically consistent motion remains a fundamental challenge. Intuitively,…
Attention Sparsity is Input-Stable: Training-Free Sparse Attention for Video Generation via Offline Sparsity Profiling and Online QK Co-Clustering
Jiayi Luo, Jiayu Chen, Jiankun Wang +6
Diffusion Transformers (DiTs) achieve strong video generation quality but suffer from high inference cost due to dense 3D attention, motivating sparse attention techniques for impr…
vLLM-Omni: Fully Disaggregated Serving for Any-to-Any Multimodal Models
Peiqi Yin, Jiangyun Zhu, Han Gao +13
Any-to-any multimodal models that jointly handle text, images, video, and audio represent a significant advance in multimodal AI. However, their complex architectures (typically co…
DriveSplat: Unified Neural Gaussian Reconstruction for Dynamic Driving Scenes
Cong Wang, Ruiqi Song, Wei Tian +3
Reconstructing large-scale dynamic driving scenes remains challenging due to the coexistence of static environments with extreme depth variation and diverse dynamic actors exhibiti…
DynamiCtrl: Rethinking the Basic Structure and the Role of Text for High-quality Human Image Animation
Haoyu Zhao, Zhongang Qi, Cong Wang +5
With diffusion transformer (DiT) excelling in video generation, its use in specific tasks has drawn increasing attention. However, adapting DiT for pose-guided human image animatio…
EasyControl: Transfer ControlNet to Video Diffusion for Controllable Generation and Interpolation
Cong Wang, Jiaxi Gu, Panwen Hu +5
Following the advancements in text-guided image generation technology exemplified by Stable Diffusion, video generation is gaining increased attention in the academic community. Ho…
T2M-HiFiGPT: Generating High Quality Human Motion from Textual Descriptions with Residual Discrete Representations
Congyi Wang
In this study, we introduce T2M-HiFiGPT, a novel conditional generative framework for synthesizing human motion from textual descriptions. This framework is underpinned by a Residu…
DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance
Cong Wang, Jiaxi Gu, Panwen Hu +3
Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods try to extend pre-trained text-guided i…