Learning Evidential Delta Denoising Scores for Video Editing

Recent DDS-based video editing methods have presented remarkable potential by enhancing traditional diffusion models. However, these methods are limited by the MSE-based isolated comparison of noises, leading to issues such as numerical sensitivity and local structure perception deficiency. Additionally, the inherent uncertainty introduced by the noise injection process in diffusion models further hinders the improvement of editing performance. To address these limitations, we propose an Evidential Video Editing (EVE) framework, which normalizes noise vectors into probability distributions, enhancing the comparability of element relationships. By leveraging evidential deep learning, EVE employs Dirichlet distributions to establish distribution-based probabilistic modeling, overcoming the constraints of single deterministic normalization probabilities. Furthermore, we introduce an uncertainty-guided local optimization strategy to capture the local uncertainty of noise and preserve local structural details, thereby improving editing precision. Extensive experiments demonstrate that our method achieves state-of-the-art performance in video editing.