[Submitted on 24 Sep 2026]
Title:MVAgent: Multi-Agent Video Generation via Consistent Condition Construction and Shot-Level Policy Optimization
View a PDF of the paper titled MVAgent: Multi-Agent Video Generation via Consistent Condition Construction and Shot-Level Policy Optimization, by Xiangyu Kong and 7 other authors
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Abstract:Multi-shot agentic video generation requires consistent character appearance, stable spatial layout across camera angles, and continuous character state between shots. When every shot is a separate request to a frozen generator, repeated text does not determine appearance, layout or state. We therefore recast the problem as condition construction and present MVAgent, a multi-agent pipeline whose agents collaborate through typed conditioning inputs. Because an environment image shows one viewpoint, a Spatial Grounding agent samples views from generated camera-traversal clips and anchors each shot to the view matching its framing. As generated shots drift from the plan, an Observer records how each shot ends in a continuity memory, from which a Transition agent builds character action and spatial references for the next shot. An Orchestrator composes these inputs into each request. Since a request reveals its effect only after rendering, we train it by agentic reinforcement learning with Trunk-GDPO, which compares rendered candidates at every shot rather than once per video and continues the best as the trunk. With generator and judges frozen, MVAgent attains the highest cross-shot consistency and narrative-planning quality among the compared methods on ViMax-Bench and is preferred over the strongest agentic baseline in human evaluation.
Comments: 5 pages, 2 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.30609 [cs.CV]
(or arXiv:2609.30609v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.30609
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xiangyu Kong [view email] [v1] Thu, 24 Sep 2026 22:47:40 UTC (5,935 KB)
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