arXiv:2610.31983v1 Announce Type: new
Abstract: Video encoder models are increasingly used as world models for physical AI, where a policy plans by rolling out the latent, so what physics the latent...
By Ayushi Mehrotra, Sai Vidyaranya Nuthalapati
PhysicsLENS is a new dataset and benchmark designed to evaluate how well video generation models capture physical properties relevant to robotics. It consists of matched scenario pairs that share the same conditioning frame and task but differ in underlying physics, and it covers seven physical domains such as collision, gravity, and friction. The benchmark includes over 400 human-annotated labels from four video generation models, revealing that many plausible-looking videos still ignore the specified physical property.
By Isaiah Milkey, Som Sagar, Aditya Taparia, Xinyuan Liu, Jiqing Wen, Ransalu Senanayake
SnapPhysics is a training‑free framework that reconstructs 3D objects and estimates their physical properties—mass, friction, and center of gravity—from a single image. It combines instance‑level 3D reconstruction with a physics‑aware scene graph to provide geometric grounding and inter‑object relationships for vision‑language model reasoning. Experiments on 3D‑FRONT and real captured scenes show significant improvements over existing methods, enabling physically interactive mixed reality experiences without manual tuning.
By Suji Kang, Seok-Young Kim, Young Bin Kim, Taewook Ha, Dieter Schmalstieg, Shohei Mori, Woontack Woo
arXiv:2606. 28128v1 Announce Type: cross Abstract: Video generation models have emerged as a promising paradigm for embodied world simulation.
By Peiwen Zhang, Yufan Deng, Shangkun Sun, Juncheng Ma, Duomin Wang, Jonas Du, Zilin Pan, Ye Huang, Hao Liang, Songyan Huang, Ruihua Zhang, Enze Xie, Ming-Yu Liu, Daquan Zhou
GeoFlip is a new benchmark for video generation models that tests their ability to produce physically correct outcomes when the initial geometry of a scene is altered. The benchmark presents 482 pairs of scenes with controlled changes in extent, placement, and mechanism configuration, and evaluates models on event correctness, structural integrity, and similarity to reference videos. Across 16 evaluated models, the best event correctness averaged only 27.98%, highlighting significant gaps in geometry‑sensitive physical reasoning.
By Yuqi Li, Junming Liu, Bowen Hu, Ruize Niu, Huiran Duan, Yingli Tian, Hao Wu
arXiv:2610.42002v2 Announce Type: replace
Abstract: Video generation models synthesize realistic motion, but can they respond correctly to changes in initial geometry? We introduce GeoFlip, a video g...
By Yuqi Li, Junming Liu, Bowen Hu, Ruize Niu, Huiran Duan, Ming-Hsuan Yang, Yingli Tian, Hao Wu