Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models
Recent advances in visual generative models have enabled high-quality image and video generation, but evaluating these models often demands sampling hundreds or thousands of images or videos, which is computationally expensive. Existing evaluation methods also rely on rigid pipelines that overlook specific user needs and provide numerical results without clear explanations. Mimicking how humans quickly form impressions of a model's capabilities from only a few samples, we propose the Evaluation Agent framework, which employs human-like strategies for efficient, dynamic, multi-round evaluations, offering detailed, user-tailored analyses. Given a natural-language evaluation request, the age...
| Authors | Shulin Tian, Ziqi Huang, Fan Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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