ElasticTTT: Prior-Preserving Test-Time Tuning for Video Editing
Test-Time Tuning (TTT) on pretrained diffusion models has emerged as a powerful paradigm for video editing. However, there exists a foundational mismatch between the distribution-mapping nature of generative models and the single-point optimization of standard TTT. In this paper, we demonstrate that this mismatch triggers \textit{Prior Collapse}, a degenerate state where the model discards the text conditions and spatial latents, collapsing generations to the source video, or entangling the features of distinct regions. To resolve this, we propose \textbf{ElasticTTT}, a novel framework that preserves the prior generative distribution and rescues generative elasticity. Specifically, we pro...
| Authors | Yueyi Liu, Chi Zhang, Sen Cui et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 July 2026 |
| Last updated at source | 31 July 2026 |
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