arXiv:2608.22773v1 Announce Type: new
Abstract: Dynamic 3D Gaussian Splatting (3DGS) achieves photorealistic reconstruction of time-varying scenes, and recent physics-aware extensions improve extrapo...
By Shogo Sato, Takuhiro Kaneko, Shoichiro Takeda, Tomoyasu Shimada, Riku Inoue, Kazuhiko Murasaki, Ryuichi Tanida
arXiv:2609.17521v1 Announce Type: cross
Abstract: Interactive control for video generation is moving from coarse prompts toward fine-grained, physically meaningful manipulation of dynamic scenes. Yet...
By Chuhao Chen, Peter Wonka, Chaoyang Wang, Chen Wang, Qiao Feng, Sergey Tulyakov, Lingjie Liu
Interactive control for video generation is moving from coarse prompts toward fine-grained, physically meaningful manipulation of dynamic scenes. Yet existing controllable methods either require the f...
arXiv:2605.30320v2 Announce Type: replace
Abstract: Existing inverse physics methods recover physical parameters from multi-view videos, where geometric constraints across views resolve scale and 3D...
By Daniel Rho, Jun Myeong Choi, Matthew Thornton, Biswadip Dey, Roni Sengupta
arXiv:2606. 00115v1 Announce Type: cross Abstract: Bridging the gap between visual realism and physical understanding is a core challenge for video-based world models.
By Yuanyuan Wang, Wenjie Wang, Kun Zhang, Mingming Gong
STATERA is a method that adapts a pretrained video backbone with mostly frozen weights and a lightweight temporal tubelet mixer to estimate the center-of-mass (CoM) of opaque, asymmetric rigid bodies from short monocular videos. It introduces the HiddenMass Benchmark, consisting of 50K simulated MuJoCo trajectories and a 63-sequence real-world test set with calibrated CoM ground truth. In simulation, STATERA reduces normalized CoM error from 41.7% to 25.2%, and in zero-shot sim-to-real transfer, its phase‑aware variant consistently predicts movement toward the true hidden offset, improving physics capture from 2.6% to 41.0%.
By Animesh Varma