Image Diffusion Models Enable Zero-Shot Video Object Tracking Through Temporal Propagation
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50kIters
Warm and crisp on the edges. A bagel with a bit of bite.
Summary
Researchers demonstrate that image diffusion models, originally designed for image generation, contain rich semantic structures that can be repurposed for video analysis. By reinterpreting self-attention maps as semantic label propagation kernels, the models can establish pixel-level correspondences between image regions. When extended across video frames, this creates a temporal propagation kernel enabling zero-shot object tracking via segmentation. The paper introduces DRIFT, a framework that leverages pretrained image diffusion models with SAM-guided mask refinement, achieving state-of-the-art zero-shot performance on video object segmentation benchmarks.
Key quotes
· 3 pulledImage diffusion models, though originally developed for image generation, implicitly capture rich semantic structures that enable various recognition and localization tasks beyond synthesis.
Extending this mechanism across frames yields a temporal propagation kernel that enables zero-shot object tracking via segmentation in videos.
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.
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