Core Viewpoint - The article presents a novel approach to image editing by redefining it as a degenerate temporal process, leveraging video data to enhance the efficiency and effectiveness of image editing tasks [1][4]. Group 1: Current Challenges in Image Editing - Existing image editing methods based on diffusion models require large-scale, high-quality triplet data (instruction-source image-edited image), which is costly and fails to cover diverse user editing intents [3]. - There is a fundamental trade-off between structure preservation and texture modification; emphasizing one can limit flexibility while pursuing significant semantic changes may lead to geometric distortions [3]. Group 2: Video4Edit Approach - The Video4Edit project team redefines image editing as a special degenerate form of video generation, allowing for the extraction of knowledge from video data [4]. - By modeling the image editing task as a two-frame video generation process, the source image is treated as frame 0 and the edited image as frame 1, enabling the model to learn from video data and improve data efficiency [6][9]. Group 3: Knowledge Transfer and Efficiency - The use of single-frame evolution prior from video pre-trained models allows for the natural incorporation of structural preservation and semantic change balance mechanisms [7]. - The model learns to align editing intents rather than starting from scratch, leading to efficient parameter reuse and improved data efficiency [12]. Group 4: Data Efficiency Analysis - Introducing video priors significantly reduces the entropy of the hypothesis space, enhancing effective generalization capabilities [15]. - The temporal evolution-based fine-tuning offers higher sample efficiency, explaining why only about 1% of supervised data is needed to achieve convergence [16]. Group 5: Performance Evaluation - Video4Edit has been systematically evaluated across various image editing tasks, including style transfer, object replacement, and attribute modification, demonstrating its ability to accurately capture target style features while preserving source image structure [17]. - The model achieves comparable or superior performance to baseline methods using only 1% of the supervised data, indicating a significant reduction in dependency on labeled data while maintaining high-quality editing results [21][23].