VISTA: A Test-Time Self-Improving Video Generation Agent
Authors: name: Do Xuan Long, name: Xingchen Wan, name: Hootan Nakhost, name: Chen-Yu Lee, name: Tomas Pfister, name: Sercan Ö. Arık
Organization: Google

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.