VALA: Learning Latent Anchors for Training-Free and Temporally Consistent

• URL: http://arxiv.org/abs/2510.22970v1
• Date: Mon, 27 Oct 2025 03:44:11 GMT
• Title: VALA: Learning Latent Anchors for Training-Free and Temporally Consistent
• Authors: Zhangkai Wu, Xuhui Fan, Zhongyuan Xie, Kaize Shi, Longbing Cao,
• Abstract summary: We propose VALA, a variational alignment module that adaptively selects key frames and compresses their latent features into semantic anchors for consistent video editing.<n>Our method can be fully integrated into training-free text-to-image based video editing models.
• Score: 29.516179213427694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in training-free video editing have enabled lightweight and precise cross-frame generation by leveraging pre-trained text-to-image diffusion models. However, existing methods often rely on heuristic frame selection to maintain temporal consistency during DDIM inversion, which introduces manual bias and reduces the scalability of end-to-end inference. In this paper, we propose~\textbf{VALA} (\textbf{V}ariational \textbf{A}lignment for \textbf{L}atent \textbf{A}nchors), a variational alignment module that adaptively selects key frames and compresses their latent features into semantic anchors for consistent video editing. To learn meaningful assignments, VALA propose a variational framework with a contrastive learning objective. Therefore, it can transform cross-frame latent representations into compressed latent anchors that preserve both content and temporal coherence. Our method can be fully integrated into training-free text-to-image based video editing models. Extensive experiments on real-world video editing benchmarks show that VALA achieves state-of-the-art performance in inversion fidelity, editing quality, and temporal consistency, while offering improved efficiency over prior methods.

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