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Diffusion models can finally produce temporally stable video fusion by reframing the task as history-conditioned motion generation, sidestepping the limitations of optical flow and frame-by-frame processing.
Current video object segmentation models fall apart when forced to reason causally, highlighting a critical gap between research benchmarks and real-world applicability.
