ShareVerse: Multi-Agent Consistent Video Generation for Shared World Modeling

• URL: http://arxiv.org/abs/2603.02697v1
• Date: Tue, 03 Mar 2026 07:41:12 GMT
• Title: ShareVerse: Multi-Agent Consistent Video Generation for Shared World Modeling
• Abstract summary: This paper presents ShareVerse, a video generation framework enabling multi-agent shared world modeling.<n>A dataset for large-scale interactive world modeling is built on the CARLA simulation platform.<n>We propose a spatial concatenation strategy for four-view videos of independent agents to model a broader environment.<n>We integrate cross-agent attention blocks into the pretrained video model, which enable interactive transmission of spatial-temporal information across agents.
• Score: 16.27470663877285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. ShareVerse leverages the generation capability of large video models and integrates three key innovations: 1) A dataset for large-scale multi-agent interactive world modeling is built on the CARLA simulation platform, featuring diverse scenes, weather conditions, and interactive trajectories with paired multi-view videos (front/ rear/ left/ right views per agent) and camera data. 2) We propose a spatial concatenation strategy for four-view videos of independent agents to model a broader environment and to ensure internal multi-view geometric consistency. 3) We integrate cross-agent attention blocks into the pretrained video model, which enable interactive transmission of spatial-temporal information across agents, guaranteeing shared world consistency in overlapping regions and reasonable generation in non-overlapping regions. ShareVerse, which supports 49-frame large-scale video generation, accurately perceives the position of dynamic agents and achieves consistent shared world modeling.

Related papers

• Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers [50.3000377325823]
  We present WorldWeaver, a streaming multi-agent video diffusion model that augments rollout with cross-agent world state registers.<n>We ground these registers with supervision signals spanning individual agent status, global state views including bird's-eye views, and scene text.<n>Experiments in two-agent Minecraft video generation show that explicit world-state modeling improves logical consistency and generation quality.
  arXiv  Detail & Related papers  (2026-07-23T17:59:58Z)
• Prisma-World: Camera-Controllable Multi-Agent Video World Model [67.72842238020192]
  We introduce Prisma-World, a camera-controllable multi-agent world model.<n>It formulates multi-agent generation as a joint geometry-aware denoising process for cross-view consistency.<n>Experiments show that a single Prisma-World model can generate high-fidelity multi-agent videos with flexible agent numbers.
  arXiv  Detail & Related papers  (2026-06-08T13:59:50Z)
• MetaWorld: Scaling Multi-Agent Video World Model from Single-view Video Data [125.43597497646444]
  MetaWorld is a novel framework that scales multi-agent video world models to open-domain environments directly from single-view videos.<n>It achieves superior cross-view consistency and identity fidelity, establishing a highly scalable, physics-driven paradigm for multi-agent video world modeling.
  arXiv  Detail & Related papers  (2026-06-01T18:20:20Z)
• V2XCrafter: Learning to Generate Driving Scene Across Agents [52.22336670088219]
  Collaborative driving systems leverage vehicle-to-everything (V2X) communication for collaborative perception to enhance driving safety.<n>We propose V2XCrafter, the first framework for generating controllable and realistic collaborative driving scene across agents' camera views.<n>Experiments have shown that V2XCrafter can generate high-fidelity and controllable street views with consistency across agents, thereby effectively enhancing the downstream collaborative 3D object detection tasks.
  arXiv  Detail & Related papers  (2026-05-28T07:03:51Z)
• MultiWorld: Scalable Multi-Agent Multi-View Video World Models [36.970254855945484]
  We present textbfMultiWorld, a unified framework for multi-agent multi-view world modeling.<n>We introduce the Multi-Agent Condition Module to achieve precise multi-agent controllability.<n>Experiments on multi-player game environments and multi-robot manipulation tasks demonstrate that MultiWorld outperforms baselines in video fidelity, action-following ability, and multi-view consistency.
  arXiv  Detail & Related papers  (2026-04-20T17:52:15Z)
• A Versatile Multimodal Agent for Multimedia Content Generation [66.86040734610073]
  We propose a MultiMedia-Agent designed to automate complex content creation tasks.<n>Our agent system includes a data generation pipeline, a tool library for content creation, and a set of metrics for evaluating preference alignment.
  arXiv  Detail & Related papers  (2026-01-06T18:49:47Z)
• Diffusion Forcing for Multi-Agent Interaction Sequence Modeling [52.769202433667125]
  MAGNet is a unified autoregressive diffusion framework for multi-agent motion generation.<n>It supports a wide range of interaction tasks through flexible conditioning and sampling.<n>It captures both tightly synchronized activities and loosely structured social interactions.
  arXiv  Detail & Related papers  (2025-12-19T18:59:02Z)
• IC-World: In-Context Generation for Shared World Modeling [61.69655562995357]
  Video-based world models have recently garnered increasing attention for their ability to synthesize diverse and dynamic visual environments.<n>In this paper, we focus on shared world modeling, where a model generates multiple videos from a set of input images, each representing the same underlying world in different camera poses.<n>We propose IC-World, a novel generation framework, enabling parallel generation for all input images.
  arXiv  Detail & Related papers  (2025-12-01T16:52:02Z)
• Yan: Foundational Interactive Video Generation [25.398980906541524]
  Yan is a foundational framework for interactive video generation, covering the entire pipeline from simulation and generation to editing.<n>We design a highly-compressed, low-latency 3D-VAE coupled with a KV-cache-based shift-window denoising inference process.<n>We propose a hybrid model that explicitly disentangles interactive mechanics simulation from visual rendering, enabling multi-granularity video content editing during interaction through text.
  arXiv  Detail & Related papers  (2025-08-12T03:34:21Z)
• VideoFusion: A Spatio-Temporal Collaborative Network for Multi-modal Video Fusion and Restoration [26.59510171451438]
  Existing multi-sensor fusion research predominantly integrates complementary from multiple images rather than videos.<n>VideoFusion exploits cross-modal complementarity and temporal dynamics to generate context-temporally coherent videos.<n>Extensive experiments reveal that VideoFusion outperforms existing image-oriented fusion paradigms in sequential scenarios.
  arXiv  Detail & Related papers  (2025-03-30T08:27:18Z)
• iVideoGPT: Interactive VideoGPTs are Scalable World Models [70.02290687442624]
  World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. This work introduces Interactive VideoGPT, a scalable autoregressive transformer framework that integrates multimodal signals--visual observations, actions, and rewards--into a sequence of tokens. iVideoGPT features a novel compressive tokenization technique that efficiently discretizes high-dimensional visual observations.
  arXiv  Detail & Related papers  (2024-05-24T05:29:12Z)
• SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation [61.392147185793476]
  We present a unified and versatile foundation model, namely, SEED-X.<n>SEED-X is able to model multi-granularity visual semantics for comprehension and generation tasks.<n>We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications.
  arXiv  Detail & Related papers  (2024-04-22T17:56:09Z)
• MHMS: Multimodal Hierarchical Multimedia Summarization [80.18786847090522]
  We propose a multimodal hierarchical multimedia summarization (MHMS) framework by interacting visual and language domains. Our method contains video and textual segmentation and summarization module, respectively. It formulates a cross-domain alignment objective with optimal transport distance to generate the representative and textual summary.
  arXiv  Detail & Related papers  (2022-04-07T21:00:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.