In practical AI workflows, complex tasks often involve chaining multiple generative models, such as using a video or 3D generation model after a 2D image generator. However, distributional mismatches between the output of upstream models and the expected input of downstream models frequently degrade overall generation quality. To address this issue, we propose Uni-Classifier (Uni-C), a simple yet effective plug-and-play module that leverages video diffusion priors to guide the denoising process of preceding models, thereby aligning their outputs with downstream requirements. Uni-C can also be applied independently to enhance the output quality of individual generative models. Extensive experiments across video and 3D generation tasks demonstrate that Uni-C consistently improves generation quality in both workflow-based and standalone settings, highlighting its versatility and strong generalization capability.
@article{arxiv.2603.20382,
title = {Uni-Classifier: Leveraging Video Diffusion Priors for Universal Guidance Classifier},
author = {Yujie Zhou and Pengyang Ling and Jiazi Bu and Bingjie Gao and Li Niu},
journal= {arXiv preprint arXiv:2603.20382},
year = {2026}
}