Physics-Driven AIGC: Physically-Consistent Video, 4D Scene, and World Generation

Recent advances in AI-Generated Content (AIGC), including diffusion and flow-based generative models, have enabled impressive progress in video synthesis, dynamic scene generation, and emerging world-modeling systems. Despite remarkable visual realism, most existing models still lack explicit physical grounding and frequently produce temporally inconsistent motions, implausible interactions, and violations of fundamental physical laws such as gravity, momentum, and collision constraints. These limitations significantly restrict the applicability of AIGC in emerging multimedia scenarios that require stable, long-horizon, and physically coherent dynamics, including XR/VR, digital twins, robotics simulation, and interactive virtual environments.

This workshop, Physics-Driven AIGC: Physically-Consistent Video, 4D Scene, and World Generation, aims to establish a dedicated forum for exploring generative models that integrate physical priors, dynamic constraints, and causal structures into multimedia content creation. The workshop focuses on bridging generative modeling with physics-informed reasoning, addressing challenges in dynamics-consistent video generation, 4D scene and world modeling, and physically grounded evaluation. Topics include physics-driven diffusion and flow-matching models, physical constraints and energy consistency, scene evolution and human–object interaction, physics-informed score distillation, efficient physics-aware AIGC, and benchmarks for physical consistency.

By bringing together researchers from multimedia, computer vision, graphics, simulation, and machine learning, this workshop seeks to foster cross-disciplinary collaboration and advance the development of physically-consistent, reliable, and controllable AIGC systems. As the first ICME workshop explicitly centered on physics-driven AIGC, it fills a critical gap in the current landscape and aligns closely with ICME’s mission to advance multimedia technologies, systems, and applications.

More details will be announced soon.

Researchers are welcome to submit their work in this workshop. More details will be announced soon. 

Committee members:

Dr. Ping Liu, University of Nevada, Reno, pingl at unr.edu

Dr. Yawei Luo, Zhejiang University, yaweiluo at zju.edu.cn

Dr. Feifei Shao, Zhejiang University, sff at zju.edu.cn

Dr. Jun Xiao, Zhejiang University, junx at cs.zju.edu.cn