The 1st Workshop on Physical AI brings together researchers across vision, graphics, robotics, and generative modeling to advance physics-grounded understanding of the real world — from physical property estimation and 3D/4D reconstruction to differentiable simulation and physically plausible generation.
Physical AI seeks to endow AI systems with a deep, physics-grounded understanding of the real world. While recent advances in computer vision have enabled large-scale geometric reconstruction, multimodal reasoning, and generative modeling, most systems remain limited in modeling physical properties — mass, friction, material behavior, structural stability, deformation, and dynamic interactions.
This workshop integrates physical reasoning throughout the pipeline: physical property estimation, physics-informed 3D/4D reconstruction, differentiable simulation, physically plausible generation, and embodied interaction. Rather than treating physics as a downstream refinement, PhysAI positions it as a core inductive bias for representation learning and world modeling.
The workshop fosters interdisciplinary discussion across vision, graphics, robotics, and digital twinning, and is built around 14 invited talks, a panel discussion, and a competition on dynamic 4D reconstruction.
Physical attribute estimation and reasoning — materials, mass, friction, affordances.
Reconstruction from sparse or unconstrained inputs that respects physical priors.
Generative models and world simulators for physically consistent world modeling — a key recent goal of physical-world AI.
Reasoning from images, videos, and multi-modal data about physical behavior.
Robot learning in physics-grounded virtual environments and digital twins.
New benchmarks and datasets for physical scene understanding and reconstruction.
A line-up of leaders shaping the next generation of physics-grounded AI. Listed alphabetically.
* Speaker list is tentative; confirmations to be announced.
Full-day workshop · 14 invited talks · 2 coffee breaks · 45-min panel discussion · competition highlights.
Times are local to ECCV 2026 venue. Final program will be released closer to the event.
A two-track benchmark for reconstructing the physical world in motion.
A team spanning vision, graphics, robotics, and generative modeling — across Oxford VGG, NTU PVG, NTU MMLab, ETH Zurich, Google DeepMind, and NAVER LABS Europe.
For questions about the workshop, the challenge, or sponsorship opportunities, please reach out: