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A unified theoretical framework is introduced that separates distribution modeling from matching discrepancy and connects global objectives to pointwise feature updates through Wasserstein gradient flow and motivates MGFlow, which models feature distributions with Gaussian mixtures at an adjustable granularity between...
This framework minimizes maximum mean discrepancy in frozen self-supervised video representation spaces in frozen self-supervised video representation spaces, using a hybrid Nystr\"om--Monte Carlo estimator to balance approximation bias and sampling variance.
InteracVid is introduced, the first open-source large-scale dataset that addresses this missing supervision, so that every sample couples a preceding audio-visual context and an external stimulus with the realinteractive response that follows.
This paper proposes ElasticTTT, a novel framework that preserves the prior generative distribution and rescues generative elasticity in standard TTT, achieving state-of-the-art performance on one-shot video editing.
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