FramePainter: Endowing Interactive Image Editing with Video Diffusion Priors

• URL: http://arxiv.org/abs/2501.08225v1
• Date: Tue, 14 Jan 2025 16:09:16 GMT
• Title: FramePainter: Endowing Interactive Image Editing with Video Diffusion Priors
• Authors: Yabo Zhang, Xinpeng Zhou, Yihan Zeng, Hang Xu, Hui Li, Wangmeng Zuo,
• Abstract summary: We introduce FramePainter as an efficient instantiation of image-to-video generation problem. It only uses a lightweight sparse control encoder to inject editing signals. It domainantly outperforms previous state-of-the-art methods with far less training data.
• Score: 64.54220123913154
• License:
• Abstract: Interactive image editing allows users to modify images through visual interaction operations such as drawing, clicking, and dragging. Existing methods construct such supervision signals from videos, as they capture how objects change with various physical interactions. However, these models are usually built upon text-to-image diffusion models, so necessitate (i) massive training samples and (ii) an additional reference encoder to learn real-world dynamics and visual consistency. In this paper, we reformulate this task as an image-to-video generation problem, so that inherit powerful video diffusion priors to reduce training costs and ensure temporal consistency. Specifically, we introduce FramePainter as an efficient instantiation of this formulation. Initialized with Stable Video Diffusion, it only uses a lightweight sparse control encoder to inject editing signals. Considering the limitations of temporal attention in handling large motion between two frames, we further propose matching attention to enlarge the receptive field while encouraging dense correspondence between edited and source image tokens. We highlight the effectiveness and efficiency of FramePainter across various of editing signals: it domainantly outperforms previous state-of-the-art methods with far less training data, achieving highly seamless and coherent editing of images, \eg, automatically adjust the reflection of the cup. Moreover, FramePainter also exhibits exceptional generalization in scenarios not present in real-world videos, \eg, transform the clownfish into shark-like shape. Our code will be available at https://github.com/YBYBZhang/FramePainter.

Related papers

• Temporally Consistent Object Editing in Videos using Extended Attention [9.605596668263173]
  We propose a method to edit videos using a pre-trained inpainting image diffusion model. We ensure that the edited information will be consistent across all the video frames.
  arXiv  Detail & Related papers  (2024-06-01T02:31:16Z)
• Efficient Image Pre-Training with Siamese Cropped Masked Autoencoders [89.12558126877532]
  We propose CropMAE, an alternative approach to the Siamese pre-training introduced by SiamMAE. Our method exclusively considers pairs of cropped images sourced from the same image but cropped differently, deviating from the conventional pairs of frames extracted from a video. CropMAE achieves the highest masking ratio to date (98.5%), enabling the reconstruction of images using only two visible patches.
  arXiv  Detail & Related papers  (2024-03-26T16:04:19Z)
• VASE: Object-Centric Appearance and Shape Manipulation of Real Videos [108.60416277357712]
  In this work, we introduce a framework that is object-centric and is designed to control both the object's appearance and, notably, to execute precise and explicit structural modifications on the object. We build our framework on a pre-trained image-conditioned diffusion model, integrate layers to handle the temporal dimension, and propose training strategies and architectural modifications to enable shape control. We evaluate our method on the image-driven video editing task showing similar performance to the state-of-the-art, and showcasing novel shape-editing capabilities.
  arXiv  Detail & Related papers  (2024-01-04T18:59:24Z)
• MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers [30.924202893340087]
  State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. This paper breaks down the text-based video editing task into two stages. First, we leverage an pre-trained text-to-image diffusion model to simultaneously edit fews in a zero-shot way. Second, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers.
  arXiv  Detail & Related papers  (2023-12-19T07:05:39Z)
• 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)
• WAIT: Feature Warping for Animation to Illustration video Translation using GANs [12.681919619814419]
  We introduce a new problem for video stylizing where an unordered set of images are used. Most of the video-to-video translation methods are built on an image-to-image translation model. We propose a new generator network with feature warping layers which overcomes the limitations of the previous methods.
  arXiv  Detail & Related papers  (2023-10-07T19:45:24Z)
• TokenFlow: Consistent Diffusion Features for Consistent Video Editing [27.736354114287725]
  We present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method.
  arXiv  Detail & Related papers  (2023-07-19T18:00:03Z)
• 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)
• Pix2Video: Video Editing using Image Diffusion [43.07444438561277]
  We investigate how to use pre-trained image models for text-guided video editing. Our method works in two simple steps: first, we use a pre-trained structure-guided (e.g., depth) image diffusion model to perform text-guided edits on an anchor frame. We demonstrate that realistic text-guided video edits are possible, without any compute-intensive preprocessing or video-specific finetuning.
  arXiv  Detail & Related papers  (2023-03-22T16:36:10Z)
• FateZero: Fusing Attentions for Zero-shot Text-based Video Editing [104.27329655124299]
  We propose FateZero, a zero-shot text-based editing method on real-world videos without per-prompt training or use-specific mask. Our method is the first one to show the ability of zero-shot text-driven video style and local attribute editing from the trained text-to-image model.
  arXiv  Detail & Related papers  (2023-03-16T17:51:13Z)

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.