VQQA: An Agentic Approach for Video Evaluation and Quality Improvement
Despite rapid advancements in video generation models, aligning their outputs with complex user intent remains challenging. Existing test-time optimization methods are typically either computationally expensive or require white-box access to model internals. To address this, we present VQQA (Video Quality Question Answering), a unified, multi-agent framework generalizable across diverse input modalities and video generation tasks. By dynamically generating visual questions and using the resulting Vision-Language Model (VLM) critiques as semantic gradients, VQQA replaces traditional, passive evaluation metrics with human-interpretable, actionable feedback. This enables a highly efficient, closed-loop prompt optimization process via a black-box natural language interface. Extensive experiments demonstrate that VQQA effectively isolates and resolves visual artifacts, substantially improving generation quality in just a few refinement steps. Applicable to both text-to-video (T2V) and image-to-video (I2V) tasks, our method achieves absolute improvements of +11.57% on T2V-CompBench and +8.43% on VBench2 over vanilla generation, significantly outperforming state-of-the-art stochastic search and prompt optimization techniques.
69b7638291a28162b233fbf8
Yiwen Song
admin_assigned
2026-03-16T11:10:22.360Z
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/avatars/0612e73b2d3fda6dd4b342a21cd183fc.svg
Yiwen Song
EchoSong
user
EchoSong
69b7638291a28162b233fbf9
Tomas Pfister
admin_assigned
2026-03-16T11:10:28.875Z
68e3f17160026cedfe38266b
/avatars/ebecb599788f53d98076d4e37a060f19.svg
Tomas Pfister
tpfister
user
tpfister
69b7638291a28162b233fbfa
Yale Song
admin_assigned
2026-03-16T11:10:35.331Z
62c901d3069cb0a83ef4c4e9
/avatars/9055ac96ddded5aa98ff69a13df29bdf.svg
Yale Song
yalesong
user
yalesong
VQQA: An Agentic Approach for Video Evaluation and Quality Improvement
Despite rapid advancements in video generation models, aligning their outputs with complex user intent remains challenging. Existing test-time optimization methods are typically either computationally expensive or require white-box access to model internals. To address this, we present VQQA (Video Quality Question Answering), a unified, multi-agent framework generalizable across diverse input modalities and video generation tasks. By dynamically generating visual questions and using the resulting Vision-Language Model (VLM) critiques as semantic gradients, VQQA replaces traditional, passive evaluation metrics with human-interpretable, actionable feedback. This enables a highly efficient, closed-loop prompt optimization process via a black-box natural language interface. Extensive experiments demonstrate that VQQA effectively isolates and resolves visual artifacts, substantially improving generation quality in just a few refinement steps. Applicable to both text-to-video (T2V) and image-to-video (I2V) tasks, our method achieves absolute improvements of +11.57% on T2V-CompBench and +8.43% on VBench2 over vanilla generation, significantly outperforming state-of-the-art stochastic search and prompt optimization techniques.
VQQA is a multi-agent framework that uses vision-language model critiques as semantic gradients for efficient, black-box video generation optimization through natural language interfaces.
video generation models test-time optimization Vision-Language Model semantic gradients prompt optimization natural language interface video quality question answering multi-agent framework