arXiv AI
Jun 29
PhysisForcing: Physics Reinforced World Simulator for Robotic Manipulation
arXiv:2606. 28128v1 Announce Type: cross Abstract: Video generation models have emerged as a promising paradigm for embodied world simulation.
Learning Explicit Physical Parameter Control and Benchmarking for Video Generation
Recent advances in image-to-video generation have improved visual realism, making physically grounded and controllable dynamics an important step toward future world simulation. Current models often generate plausible motion, but it is not reliably governed by explicit physical causes, and instance-level constraints can leak or become entangled in multi-object interactions.
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arXiv AI
Jun 17
Phys4D: Fine-Grained Physics-Consistent 4D Modeling from Video Diffusion
arXiv:2603. 03485v3 Announce Type: replace-cross Abstract: Recent video diffusion models have achieved impressive capabilities as large-scale generative world models.
arXiv AI
Jun 16
NEXUS: Neural Energy Fields for Physically Consistent Contact-Rich 3D Object Dynamics
arXiv:2606. 15015v1 Announce Type: cross Abstract: Physics-grounded video generation requires controllable 3D object dynamics that remain physically consistent under contact, deformation, and external forcing.
arXiv AI
Aug 7
GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models
arXiv:2608. 05948v1 Announce Type: new Abstract: Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit simulators of future states and interactions.
Hugging Face Trending Papers
Jul 21
DeforM: Reasoning-Guided Physics-Aware Video Generation via Spatial-Temporal Masking
Video generation models achieve high visual quality but often struggle to generate physics-aware videos. Unlike rigid-body motion, which can be described by explicit trajectories or formulas, complex deformation dynamics remain challenging to synthesize.
Hugging Face Trending Papers
Jul 29
CG-World: A Large-Scale World-State Dataset and Protocol for World Models
World models must learn the joint dynamics of states, actions, events, and observations, yet existing video, robotics, and simulation datasets usually capture only part of this structure. We introduce CG-World, a large-scale world-state dataset and protocol derived from industrial computer graphics production pipelines.
arXiv AI
Jul 31
CG-World: A Large-Scale World-State Dataset and Protocol for World Models
arXiv:2607. 26452v1 Announce Type: new Abstract: World models must learn the joint dynamics of states, actions, events, and observations, yet existing video, robotics, and simulation datasets usually capture only part of this structure.
Hugging Face Trending Papers
Aug 6
GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models
Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit simulators of future states and interactions. However, existing evaluations of physical fidelity are often conducted in isolation and rely heavily on perceptual similarity or human judgments, providing limited insight into which physical principles or parameters are violated.
arXiv AI
Jul 3
Learning 3D-Gaussian Simulators from RGB Videos
arXiv:2503. 24009v3 Announce Type: replace-cross Abstract: Realistic simulation is critical for applications ranging from robotics to animation.
Hugging Face Trending Papers
Jul 27
DeVA: Decoupled Video-Action Model with physical guidance for robot policy learning
Generalizable robot manipulation requires policies that can anticipate how visual scenes evolve while executing language instructions. While recent Vision-Language-Action models benefit from large-scale pretraining, their predominantly static pretraining objectives provide limited supervision for physical dynamics and temporal causality, leaving control-relevant knowledge to be learned from downstream robot demonstrations.
Hugging Face Trending Papers
Jul 6
Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models
Predicting object dynamics (i. e.
arXiv AI
Jun 16
EgoPhys: Learning Generalizable Physics Models of Deformable Objects from Egocentric Video
arXiv:2606. 16202v1 Announce Type: cross Abstract: Humans naturally understand object physics through everyday interactions, but faithfully predicting complex deformable dynamics, such as elastic materials and fabrics, remains a major challenge for computer vision and robotics.