Magic Fixup: Streamlining Photo Editing by Watching Dynamic Videos
- URL: http://arxiv.org/abs/2403.13044v1
- Date: Tue, 19 Mar 2024 17:59:58 GMT
- Title: Magic Fixup: Streamlining Photo Editing by Watching Dynamic Videos
- Authors: Hadi Alzayer, Zhihao Xia, Xuaner Zhang, Eli Shechtman, Jia-Bin Huang, Michael Gharbi,
- Abstract summary: We propose a generative model that synthesizes a photorealistic output that follows a prescribed layout.
Our method transfers fine details from the original image and preserves the identity of its parts.
We show that by using simple segmentations and coarse 2D manipulations, we can synthesize a photorealistic edit faithful to the user's input.
- Score: 32.74215702447293
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a generative model that, given a coarsely edited image, synthesizes a photorealistic output that follows the prescribed layout. Our method transfers fine details from the original image and preserves the identity of its parts. Yet, it adapts it to the lighting and context defined by the new layout. Our key insight is that videos are a powerful source of supervision for this task: objects and camera motions provide many observations of how the world changes with viewpoint, lighting, and physical interactions. We construct an image dataset in which each sample is a pair of source and target frames extracted from the same video at randomly chosen time intervals. We warp the source frame toward the target using two motion models that mimic the expected test-time user edits. We supervise our model to translate the warped image into the ground truth, starting from a pretrained diffusion model. Our model design explicitly enables fine detail transfer from the source frame to the generated image, while closely following the user-specified layout. We show that by using simple segmentations and coarse 2D manipulations, we can synthesize a photorealistic edit faithful to the user's input while addressing second-order effects like harmonizing the lighting and physical interactions between edited objects.
Related papers
- HOI-Swap: Swapping Objects in Videos with Hand-Object Interaction Awareness [57.18183962641015]
We present HOI-Swap, a video editing framework trained in a self-supervised manner.
The first stage focuses on object swapping in a single frame with HOI awareness.
The second stage extends the single-frame edit across the entire sequence.
arXiv Detail & Related papers (2024-06-11T22:31:29Z) - Zero-shot Image Editing with Reference Imitation [50.75310094611476]
We present a new form of editing, termed imitative editing, to help users exercise their creativity more conveniently.
We propose a generative training framework, dubbed MimicBrush, which randomly selects two frames from a video clip, masks some regions of one frame, and learns to recover the masked regions using the information from the other frame.
We experimentally show the effectiveness of our method under various test cases as well as its superiority over existing alternatives.
arXiv Detail & Related papers (2024-06-11T17:59:51Z) - 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) - Unified Editing of Panorama, 3D Scenes, and Videos Through Disentangled Self-Attention Injection [60.47731445033151]
We propose a novel unified editing framework that combines the strengths of both approaches by utilizing only a basic 2D image text-to-image (T2I) diffusion model.
Experimental results confirm that our method enables editing across diverse modalities including 3D scenes, videos, and panorama images.
arXiv Detail & Related papers (2024-05-27T04:44:36Z) - Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices [19.07572422897737]
We present Slicedit, a method for text-based video editing that utilize a pretrained T2I diffusion model to process both spatial andtemporal slices.
Our method generates videos retain the structure and motion of the original video while adhering to the target text.
arXiv Detail & Related papers (2024-05-20T17:55:56Z) - 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) - MotionEditor: Editing Video Motion via Content-Aware Diffusion [96.825431998349]
MotionEditor is a diffusion model for video motion editing.
It incorporates a novel content-aware motion adapter into ControlNet to capture temporal motion correspondence.
arXiv Detail & Related papers (2023-11-30T18:59:33Z) - 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) - EdiBERT, a generative model for image editing [12.605607949417033]
EdiBERT is a bi-directional transformer trained in the discrete latent space built by a vector-quantized auto-encoder.
We show that the resulting model matches state-of-the-art performances on a wide variety of tasks.
arXiv Detail & Related papers (2021-11-30T10:23:06Z)
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.