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Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing autoregressive video diffusion pipelines carry forward observation history as conditioning context, which makes shared state difficult to maintain in multi-agent and multi-view settings.
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.
arXiv:2604. 02330v2 Announce Type: replace-cross Abstract: Recent advances in video diffusion have enabled the development of "world models" capable of simulating interactive environments.
arXiv:2603. 02697v2 Announce Type: replace-cross Abstract: This paper presents ShareVerse, a video generation framework enabling multi-agent shared world modeling, addressing the gap in existing works that lack support for unified shared world construction with multi-agent interaction.
TAPESTRY repurposes a pretrained video diffusion transformer into a robotic policy by treating robot actions as single-patch tokens in the model’s latent space, eliminating the need for video generation at inference. The lightweight linear projection maps each action timestep to the VAE patch dimensions, and the shared backbone is trained with three diffusion objectives—video diffusion, joint video+action diffusion, and action-only diffusion—to learn dynamics priors and action denoising. At test time, the model produces smooth action chunks in a single DDIM step, achieving strong performance on simulation and real‑robot benchmarks with significantly lower inference cost than comparable joint video–action policies.
arXiv:2508.13009v5 Announce Type: replace Abstract: Recent advances in interactive video generations have demonstrated diffusion model's potential as world models by capturing complex physical dynami...