PhysiGen: Integrating Collision-Aware Physical Constraints for High-Fidelity Human-Human Interaction Generation
Sun Yat-sen University（中山大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Shanghai Jiao Tong University（上海交通大学）；Guilin University of Electronic Technology（桂林电子科技大学）
Despite substantial progress in text-driven 3D human motion synthesis, generating realistic multi-person interaction sequences remains challenging. Notably, body inter-penetration is a pervasive issue from both data acquisition to the generated results, which significantly undermines the realism and usability. Previous generative models either ignored this issue or introduced computationally expensive mesh-level loss functions to alleviate inter-body collisions. In this paper, we propose a general-purpose and computationally efficient optimization strategy named PhysiGen to explicitly integrate collision-aware physical constraints for human-human interaction generation. Specifically, we simplify the high-resolution human body mesh into geometric primitives to greatly reduce the cost of inter-person collision detection. Moreover, we identify the collision regions as the guidance of the optimization directions. PhysiGen is plug-and-play and can be readily integrated into existing human interaction generation models. Extensive cross-dataset and cross-model experiments show that our method can effectively reduce interpenetration and significantly improve visual coherence and physical plausibility compared to the state-of-the-art methods.
PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；School of Artificial Intelligence, Beijing Normal University（人工智能学院，北京师范大学）
GarmentX: Autoregressive Parametric Representations for High-Fidelity 3D Garment Generation
South China University of Technology（华南理工大学）；National University of Singapore（新加坡国立大学）；LIGHTSPEED（光速（公司））；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The University of Hong Kong（香港大学）
CognitionCapturerPro: Towards High-Fidelity Visual Decoding from EEG/MEG via Multi-modal Information and Asymmetric Alignment
Visual Information Processing Laboratory, School of Electronic Engineering, Xidian University（电子科技大学信息处理实验室，电子工程学院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
KHMP: Frequency-Domain Kalman Refinement for High-Fidelity Human Motion Prediction
School of Engineering, Yunnan University（云南大学工程学院）；Department of Computer Science, University of North Carolina at Charlotte（北卡罗来纳州立大学计算机科学系）；Department of Computer Science, Northern Illinois University（北伊利诺伊大学计算机科学系）；Institute of Artificial Intelligence, University of Central Florida（佛罗里达中央大学人工智能研究所）
Semantic and Temporal Integration in Latent Diffusion Space for High-Fidelity Video Super-Resolution
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China（图像通信与网络工程研究所，上海交通大学，上海，中国）；Tencent, Shanghai, China（腾讯，上海，中国）
FlowObject: Flow Steering for Bridging Generative Priors and Reconstruction Fidelity
Graz University of Technology Austria（奥地利格拉茨理工大学）；Tampere University Finland（芬兰塔尔库大学）；Technical University of Munich Germany（德国慕尼黑技术大学）；Stanford University The United States of America（美国斯坦福大学）；Xi’an Jiaotong University China（中国西安交通大学）；École des Ponts ParisTech France（法国巴黎综合理工学院）
DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance
Shenzhen Campus of Sun Yat-Sen University（中山大学深圳校区）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Shenzhen Campus of the Chinese University of Hong Kong（香港中文大学深圳校区）
High-Fidelity 4D Hand-Object Capture via Multi-View Spatiotemporal Tracking and Physics-Aware Gaussians
Google XR（谷歌XR）；University of Science and Technology of China (USTC)（中国科学技术大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Mind-Brush: Integrating Agentic Cognitive Search and Reasoning into Image Generation
Sun Yat-sen University（中山大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；MMLab, The Chinese University of Hong Kong（香港中文大学MMLab）
RGM: Reconstructing High-fidelity 3D Car Assets with Relightable 3D-GS Generative Model from a Single Image
AIR, Tsinghua University（清华大学AIR）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Beijing Jiaotong University（北京交通大学）；Beijing Institute of Technology（北京理工大学）
CtrlNeRF: The Generative Neural Radiation Fields for the Controllable Synthesis of High-fidelity 3D-Aware Images
School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；Department of Electrical and Computer Engineering, California State Polytechnic University（加州州立理工大学波莫纳分校电气与计算机工程学院）
G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors
South China University of Technology（华南理工大学）；Guangdong Engineering Center for Large Model and GenAI Technology（广东省大模型与生成式人工智能技术工程中心）；State Key Laboratory of Subtropical Building and Urban Science（亚热带建筑与城市科学国家重点实验室）；The Hong Kong Polytechnic University（香港理工大学）
SGDFuse: SAM-Guided Diffusion Model for High-Fidelity Infrared and Visible Image Fusion
School of Computer Science（计算机科学学院）；Business University, Yantai, China（烟台商学院）；College of Computer and Data Science（计算机与数据科学学院）；Fuzhou University, Fuzhou, China（福州大学）；School of Computing and Mathematical Sciences（计算与数学科学学院）；University of Leicester, Leicester, United Kingdom（莱斯特大学）；School of Computing and Artificial Intelligence（计算与人工智能学院）；Director of the Rapid-Rich Object Search Laboratory（快速丰富对象搜索实验室主任）；NTU-PKU Joint Research Institute, Nanyang Technological University, Singapore（NTU-PKU联合研究机构，新加坡国立大学）
