Gen4U: Unifying Video Generation and Understanding via Diffusion

Michael King; Aravindh Mahendran; Matthew Koichi Grimes; Fedor Kitashov; Adham Elarabawy; Pedro Velez; Maks Ovsjanikov; Viorica Pătrăucean

arXiv · 2607.06856

Gen4U: Unifying Video Generation and Understanding via Diffusion

Michael King·Aravindh Mahendran·Matthew Koichi Grimes·Fedor Kitashov·Adham Elarabawy·Pedro Velez·Maks Ovsjanikov·Viorica Pătrăucean

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Abstract

Prior work suggests that diffusion representations capture low-level geometry but struggle with high-level semantics. We demonstrate that state-of-the-art video diffusion models overcome this limitation. By systematically probing their intermediate activations using recent mutual-kNN alignment metrics, we reveal a highly structured latent space where visual representations evolve across both network depth and noise levels. We show that while moderate noise levels yield linearly separable global semantics, fine-grained details persist at lower noise levels but become spatially scattered, requiring attention mechanisms to decode. Building on these insights, we introduce Gen4U (Generation for Understanding), a framework that repurposes these generative representations with a single forward pass. Our experiments establish that frozen, large-scale video diffusion models function as highly competitive video encoders across a wide spectrum of tasks, spanning semantic and non-semantic objectives (video classification, depth estimation, camera pose estimation, image and video captioning). Bypassing fine-tuning, Gen4U unifies the generation and understanding paradigms, achieving strong perception performance while fully preserving the model's ability to generate high-quality video.

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BibTeX RIS

Michael King, Aravindh Mahendran, Matthew Koichi Grimes, Fedor Kitashov, Adham Elarabawy, Pedro Velez, Maks Ovsjanikov, Viorica Pătrăucean. 2026-07-07. Gen4U: Unifying Video Generation and Understanding via Diffusion. https://arxiv.org/abs/2607.06856

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