Zero-Shot Video Translation and Editing with Frame Spatial-Temporal Correspondence

• URL: http://arxiv.org/abs/2512.03905v1
• Date: Wed, 03 Dec 2025 15:51:11 GMT
• Title: Zero-Shot Video Translation and Editing with Frame Spatial-Temporal Correspondence
• Authors: Shuai Yang, Junxin Lin, Yifan Zhou, Ziwei Liu, Chen Change Loy,
• Abstract summary: We present FRESCO, which integrates intra-frame correspondence with inter-frame correspondence to formulate a more robust spatial-temporal constraint.<n>Our method goes beyond attention guidance to explicitly optimize features, achieving high spatial-temporal consistency with the input video.<n>We verify FRESCO adaptations on two zero-shot tasks of video-to-video translation and text-guided video editing.
• Score: 81.82643953694485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The remarkable success in text-to-image diffusion models has motivated extensive investigation of their potential for video applications. Zero-shot techniques aim to adapt image diffusion models for videos without requiring further model training. Recent methods largely emphasize integrating inter-frame correspondence into attention mechanisms. However, the soft constraint applied to identify the valid features to attend is insufficient, which could lead to temporal inconsistency. In this paper, we present FRESCO, which integrates intra-frame correspondence with inter-frame correspondence to formulate a more robust spatial-temporal constraint. This enhancement ensures a consistent transformation of semantically similar content between frames. Our method goes beyond attention guidance to explicitly optimize features, achieving high spatial-temporal consistency with the input video, significantly enhancing the visual coherence of manipulated videos. We verify FRESCO adaptations on two zero-shot tasks of video-to-video translation and text-guided video editing. Comprehensive experiments demonstrate the effectiveness of our framework in generating high-quality, coherent videos, highlighting a significant advance over current zero-shot methods.

Related papers

• VALA: Learning Latent Anchors for Training-Free and Temporally Consistent [29.516179213427694]
  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.
  arXiv  Detail & Related papers  (2025-10-27T03:44:11Z)
• Optical-Flow Guided Prompt Optimization for Coherent Video Generation [51.430833518070145]
  We propose a framework called MotionPrompt that guides the video generation process via optical flow.<n>We optimize learnable token embeddings during reverse sampling steps by using gradients from a trained discriminator applied to random frame pairs.<n>This approach allows our method to generate visually coherent video sequences that closely reflect natural motion dynamics, without compromising the fidelity of the generated content.
  arXiv  Detail & Related papers  (2024-11-23T12:26:52Z)
• COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing [57.76170824395532]
  Video editing is an emerging task, in which most current methods adopt the pre-trained text-to-image (T2I) diffusion model to edit the source video.<n>We propose COrrespondence-guided Video Editing (COVE) to achieve high-quality and consistent video editing.<n>COVE can be seamlessly integrated into the pre-trained T2I diffusion model without the need for extra training or optimization.
  arXiv  Detail & Related papers  (2024-06-13T06:27:13Z)
• FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation [85.29772293776395]
  We introduce FRESCO, intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint. This enhancement ensures a more consistent transformation of semantically similar content across frames. Our approach involves an explicit update of features to achieve high spatial-temporal consistency with the input video.
  arXiv  Detail & Related papers  (2024-03-19T17:59:18Z)
• VidToMe: Video Token Merging for Zero-Shot Video Editing [100.79999871424931]
  We propose a novel approach to enhance temporal consistency in generated videos by merging self-attention tokens across frames. Our method improves temporal coherence and reduces memory consumption in self-attention computations.
  arXiv  Detail & Related papers  (2023-12-17T09:05:56Z)
• Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation [93.18163456287164]
  This paper proposes a novel text-guided video-to-video translation framework to adapt image models to videos. Our framework achieves global style and local texture temporal consistency at a low cost.
  arXiv  Detail & Related papers  (2023-06-13T17:52:23Z)
• Adaptive Compact Attention For Few-shot Video-to-video Translation [13.535988102579918]
  We introduce a novel adaptive compact attention mechanism to efficiently extract contextual features jointly from multiple reference images. Our core idea is to extract compact basis sets from all the reference images as higher-level representations. We extensively evaluate our method on a large-scale talking-head video dataset and a human dancing dataset.
  arXiv  Detail & Related papers  (2020-11-30T11:19:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.