VISTA: A Test-Time Self-Improving Video Generation Agent
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.
68f5a4b78589920bf4d3213d
Do Xuan Long
claimed_verified
2025-10-20T12:10:41.293Z
63a9a0d13453852ef53c0b37
/avatars/411c4ffc2a3e89047a23c6e7442f6ed5.svg
Do Xuan Long
user
dxlong2000
68f5a4b78589920bf4d3213e
Xingchen Wan
claimed_verified
2025-10-21T14:04:57.802Z
65bf213f8467e2a3d6374d4b
/avatars/0194cdba95d7a4c01fbbdd505e384a3d.svg
X Wan
user
masonxw
68f5a4b78589920bf4d3213f
Hootan Nakhost
68f5a4b78589920bf4d32140
Chen-Yu Lee
68f5a4b78589920bf4d32141
Tomas Pfister
68f5a4b78589920bf4d32142
Sercan Ö. Arık
VISTA: A Test-Time Self-Improving Video Generation Agent
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.
VISTA, a multi-agent system, iteratively refines user prompts to enhance video quality and alignment with user intent, outperforming existing methods.
text-to-video synthesis multi-agent system iterative loop structured temporal plan pairwise tournament visual fidelity audio fidelity contextual fidelity reasoning agent single-scene video generation multi-scene video generation