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Computer Science > Computer Vision and Pattern Recognition

arXiv:2511.19936 (cs)
[Submitted on 25 Nov 2025]

Title:Image Diffusion Models Exhibit Emergent Temporal Propagation in
Videos

Authors:Youngseo Kim, Dohyun Kim, Geonhee Han, Paul Hongsuck Seo
View a PDF of the paper titled Image Diffusion Models Exhibit
Emergent Temporal Propagation in Videos, by Youngseo Kim and 3 other
authors
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    Abstract:Image diffusion models, though originally developed for
    image generation, implicitly capture rich semantic structures
    that enable various recognition and localization tasks beyond
    synthesis. In this work, we investigate their self-attention maps
    can be reinterpreted as semantic label propagation kernels,
    providing robust pixel-level correspondences between relevant
    image regions. Extending this mechanism across frames yields a
    temporal propagation kernel that enables zero-shot object
    tracking via segmentation in videos. We further demonstrate the
    effectiveness of test-time optimization strategies-DDIM
    inversion, textual inversion, and adaptive head weighting-in
    adapting diffusion features for robust and consistent label
    propagation. Building on these findings, we introduce DRIFT, a
    framework for object tracking in videos leveraging a pretrained
    image diffusion model with SAM-guided mask refinement, achieving
    state-of-the-art zero-shot performance on standard video object
    segmentation benchmarks.

Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2511.19936 [cs.CV]
          (or arXiv:2511.19936v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2511.19936
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Youngseo Kim [view email]
[v1] Tue, 25 Nov 2025 05:21:23 UTC (773 KB)
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