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arXiv · 2603.08403

SPIRAL: Self-Evolving Action-Conditioned Video Generation via Reflective Planning Agents

Yu Yang·Yue Liao·Jianbiao Mei·Baisen Wang·Xuemeng Yang·Licheng Wen·Jiangning Zhang·Xiangtai Li·Liang Lv·Hanlin Chen·Botian Shi·Yong Liu·Shuicheng Yan·Gim Hee Lee
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Abstract

Long-horizon action-conditioned video generation aims to synthesize temporally coherent videos that follow complex action instructions over extended horizons, requiring procedural ordering, persistent action execution, and scene consistency beyond conventional TI2V's short-term fidelity. Existing single-shot video generation models typically operate in an open-loop manner, leading to incomplete action execution, hallucinated motions, and temporal drift. To address this, we propose SPIRAL, a closed-loop framework that performs sequential planning and iterative reflection for action-conditioned long-horizon video generation. Specifically, SPIRAL instantiates a think-act-reflect process: a PlanAgent decomposes high-level goals into sub-actions, which condition a VideoGenerator to synthesize each segment alongside a memory context, while a CriticAgent evaluates intermediate video segments to provide corrective feedback for iterative refinement. This closed-loop design further supports self-evolution by utilizing PlanAgent-proposed actions and CriticAgent-derived rewards for GRPO-based post-training to enhance the video generator's long-horizon consistency. Moreover, we introduce ActVideoGen-Dataset for task-specific training, and establish ActVideoGen-Bench as a dedicated evaluation suite for measuring action quality and temporal coherence. Experiments across multiple TI2V backbones alongside the self-evolving strategy show consistent gains on ActVideoGen-Bench and VBench, demonstrating the effectiveness of SPIRAL.

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Yu Yang, Yue Liao, Jianbiao Mei, Baisen Wang, Xuemeng Yang, Licheng Wen, Jiangning Zhang, Xiangtai Li, Liang Lv, Hanlin Chen, Botian Shi, Yong Liu, Shuicheng Yan, Gim Hee Lee. 2026-03-09. SPIRAL: Self-Evolving Action-Conditioned Video Generation via Reflective Planning Agents. https://arxiv.org/abs/2603.08403

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