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arXiv · 2603.20382

Uni-Classifier: Leveraging Video Diffusion Priors for Universal Guidance Classifier

Yujie Zhou·Pengyang Ling·Jiazi Bu·Bingjie Gao·Li Niu
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Abstract

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.

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Yujie Zhou, Pengyang Ling, Jiazi Bu, Bingjie Gao, Li Niu. 2026-03-20. Uni-Classifier: Leveraging Video Diffusion Priors for Universal Guidance Classifier. https://arxiv.org/abs/2603.20382

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