Puffin-World is a unified multimodal architecture that integrates physical understanding, spatial simulation, and 3D world generation without external offline modules. It jointly models physics, geometry, and appearance as native world states and uses a unified Omni-Camera representation to support diverse tasks and flexible motions. The framework also propagates physical dynamics across future frames, couples appearance and geometry in a single generative process, and scales to complex scenarios with the Puffin-16M dataset of 15 million vision‑language‑camera triplets and 1 million trajectories.
arXiv:2610.28631v1 Announce Type: new
Abstract: Current world models for explicit world simulation typically follow two distinct pathways: (1) video generative models (e.g., Sora), which simulate rea...
By Weiqi Zhang, Wenyuan Zhang, Yu-Shen Liu, Junsheng Zhou
arXiv:2603. 03482v2 Announce Type: replace-cross Abstract: Interactive world models continually generate video by responding to a user's actions, enabling open-ended generation capabilities.
By Samuel Garcin, Thomas Walker, Steven McDonagh, Tim Pearce, Hakan Bilen, Tianyu He, Kaixin Wang, Jiang Bian
EvoWorld is a world model that generates panoramic video from a single image by combining a video generator with fine‑grained view control, a feedforward transformer that evolves an explicit 3D reconstruction, and a synthesis step that conditions on geometric reprojections from this evolving 3D memory. The model uses the evolving 3D reconstruction as spatial guidance, projecting geometry onto target viewpoints to provide rich spatial cues that improve visual realism and geometric consistency. A new benchmark covering synthetic outdoor, Habitat indoor, and real‑world scenarios evaluates long‑range exploration, loop‑closure detection, and spatial coherence, showing that EvoWorld’s evolving 3D memory enhances visual fidelity and scene coherence over existing methods.
By Jiahao Wang, Luoxin Ye, TaiMing Lu, Junfei Xiao, Jiahan Zhang, Yuxiang Guo, Xijun Liu, Rama Chellappa, Cheng Peng, Alan Yuille, Jieneng Chen
Predicting how the world evolves in response to robot actions is central to policy learning, evaluation, and planning. However, many action-conditioned world models predict future observations in 2D i...
arXiv:2610.07031v1 Announce Type: new
Abstract: Recent video world models have witnessed the paradigm shift from single-agent to multi-agent involvements, which can reveal more complicated dynamics a...
By Sitian Shen, Jiuming Liu, Mengmeng Liu, Yian Wang, Michael Ying Yang, Francesco Nex, Hao Cheng, Daniele De Martini, Ayush Tewari, Per Ola Kristensson
arXiv:2604. 01001v2 Announce Type: replace-cross Abstract: We introduce EgoSim, a closed-loop egocentric world simulator that generates spatially consistent interaction videos and persistently updates the underlying 3D scene state for continuous simulation.
By Jinkun Hao, Mingda Jia, Ruiyan Wang, Hongrui Zhu, Jiafei Cao, Xihui Liu, Ran Yi, Lizhuang Ma, Jiangmiao Pang, Xudong Xu
arXiv:2606.18250v2 Announce Type: replace
Abstract: Forecasting the evolution of dynamic environments is crucial for autonomous agents. While generative world models have achieved high photorealism i...
By Nils Morbitzer, Jonathan Evers, Artem Savkin, Thomas Stauner, Nassir Navab, Federico Tombari, Stefano Gasperini
arXiv:2606. 02753v1 Announce Type: cross Abstract: Video world models are a foundational generative technology for embodied AI and the Metaverse, yet existing approaches are inherently limited to a single agent observing from a single perspective.
By Teng Hu, Mingchun Lu, Yating Wang, Jiangning Zhang, Jinkun Hao, Ye Pan, Ran Yi, Lizhuang Ma, Dacheng Tao
WorldProbe is a new diagnostic benchmark that tests video world models by letting a goal‑conditioned multimodal agent adapt camera actions online during generation, creating 164 test cases across four probing families. The benchmark evaluates 10 state‑of‑the‑art models over more than 1,600 rollouts, totaling roughly 800 minutes and horizons up to 180 seconds, revealing that while local visual and content quality can be maintained for longer, long‑range memory and evolution remain weak. The study highlights a gap between locally stable generation and persistent world consistency that existing short‑interaction benchmarks miss.
Orbia is a new benchmark that systematically evaluates 3D consistency in interactive video world models. It organizes evaluation into three dimensions—input preservation, generated‑content persistence, and 3D self‑consistency—using controlled exploration and revisit protocols. The benchmark covers 720 instances across indoor and outdoor scenes and tests 27 different models, providing a comprehensive framework for diagnosing and improving 3D consistency.
By Dongyue Lu, Guian Fang, Hur Lim, Yonggang Wu, Yanrong Wang, Hongshan Chen, Shengju Qian, Yue Liao, Wei Chow, Lingdong Kong, Tianxin Huang, Yu Yang, Hongji Yang, Junchao Huang, Ao Liang, Yihao Wang, Zihan Wang, Rong Li, Hanlin Chen, Xin Wang, Wei Tsang Ooi, Mike Zheng Shou, Shuicheng Yan
SpatialCrafter introduces a two‑stage framework for single‑image world modeling that first generates a global 3D proxy using a Point‑anchored Sparse Structure Flow module, then refines appearance with a Generative Deferred Refiner built on a video diffusion model. The method incorporates Parallel Geometry Injection and Proxy‑Aware Corruption training to integrate the proxy without disrupting the pretrained generative manifold, and it is evaluated on a newly constructed dataset of 115K scenes. Experiments demonstrate that SpatialCrafter outperforms existing approaches, reducing long‑term drift and maintaining consistency under rapid camera motion and extreme viewpoints.
By Chuan Fang, Lingteng Qiu, Yixun Liang, Rui Chen, Kunming Luo, Zhaohua Zheng, Tongyuan Bai, Feipeng Tian, Zilong Dong, Zihan Zhou, Ping Tan