VQQA: An Agentic Approach for Video Evaluation and Quality Improvement
This addresses the problem of inefficient or inaccessible video quality evaluation for users of video generation models, offering a novel black-box optimization method.
The paper tackles the challenge of aligning video generation models with complex user intent by introducing VQQA, a multi-agent framework that uses Vision-Language Model critiques for semantic gradients, achieving absolute improvements of +11.57% on T2V-CompBench and +8.43% on VBench2 over vanilla generation.
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.