VISTA: A Test-Time Self-Improving Video Generation Agent
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917022295654400 |
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| author | Long, Do Xuan Wan, Xingchen Nakhost, Hootan Lee, Chen-Yu Pfister, Tomas Arık, Sercan Ö. |
| author_facet | Long, Do Xuan Wan, Xingchen Nakhost, Hootan Lee, Chen-Yu Pfister, Tomas Arık, Sercan Ö. |
| contents | Despite rapid advances in text-to-video synthesis, generated video quality remains critically dependent on precise user prompts. Existing test-time optimization methods, successful in other domains, struggle with the multi-faceted nature of video. In this work, we introduce VISTA (Video Iterative Self-improvemenT Agent), a novel multi-agent system that autonomously improves video generation through refining prompts in an iterative loop. VISTA first decomposes a user idea into a structured temporal plan. After generation, the best video is identified through a robust pairwise tournament. This winning video is then critiqued by a trio of specialized agents focusing on visual, audio, and contextual fidelity. Finally, a reasoning agent synthesizes this feedback to introspectively rewrite and enhance the prompt for the next generation cycle. Experiments on single- and multi-scene video generation scenarios show that while prior methods yield inconsistent gains, VISTA consistently improves video quality and alignment with user intent, achieving up to 60% pairwise win rate against state-of-the-art baselines. Human evaluators concur, preferring VISTA outputs in 66.4% of comparisons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15831 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VISTA: A Test-Time Self-Improving Video Generation Agent Long, Do Xuan Wan, Xingchen Nakhost, Hootan Lee, Chen-Yu Pfister, Tomas Arık, Sercan Ö. Computer Vision and Pattern Recognition Despite rapid advances in text-to-video synthesis, generated video quality remains critically dependent on precise user prompts. Existing test-time optimization methods, successful in other domains, struggle with the multi-faceted nature of video. In this work, we introduce VISTA (Video Iterative Self-improvemenT Agent), a novel multi-agent system that autonomously improves video generation through refining prompts in an iterative loop. VISTA first decomposes a user idea into a structured temporal plan. After generation, the best video is identified through a robust pairwise tournament. This winning video is then critiqued by a trio of specialized agents focusing on visual, audio, and contextual fidelity. Finally, a reasoning agent synthesizes this feedback to introspectively rewrite and enhance the prompt for the next generation cycle. Experiments on single- and multi-scene video generation scenarios show that while prior methods yield inconsistent gains, VISTA consistently improves video quality and alignment with user intent, achieving up to 60% pairwise win rate against state-of-the-art baselines. Human evaluators concur, preferring VISTA outputs in 66.4% of comparisons. |
| title | VISTA: A Test-Time Self-Improving Video Generation Agent |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.15831 |