MagicEdit: High-Fidelity and Temporally Coherent Video Editing
- URL: http://arxiv.org/abs/2308.14749v1
- Date: Mon, 28 Aug 2023 17:56:22 GMT
- Title: MagicEdit: High-Fidelity and Temporally Coherent Video Editing
- Authors: Jun Hao Liew and Hanshu Yan and Jianfeng Zhang and Zhongcong Xu and
Jiashi Feng
- Abstract summary: We present MagicEdit, a surprisingly simple yet effective solution to the text-guided video editing task.
We found that high-fidelity and temporally coherent video-to-video translation can be achieved by explicitly disentangling the learning of content, structure and motion signals during training.
- Score: 70.55750617502696
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this report, we present MagicEdit, a surprisingly simple yet effective
solution to the text-guided video editing task. We found that high-fidelity and
temporally coherent video-to-video translation can be achieved by explicitly
disentangling the learning of content, structure and motion signals during
training. This is in contradict to most existing methods which attempt to
jointly model both the appearance and temporal representation within a single
framework, which we argue, would lead to degradation in per-frame quality.
Despite its simplicity, we show that MagicEdit supports various downstream
video editing tasks, including video stylization, local editing, video-MagicMix
and video outpainting.
Related papers
- I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models [18.36472998650704]
We introduce a novel and generic solution that extends the applicability of image editing tools to videos by propagating edits from a single frame to the entire video using a pre-trained image-to-video model.
Our method, dubbed I2VEdit, adaptively preserves the visual and motion integrity of the source video depending on the extent of the edits.
arXiv Detail & Related papers (2024-05-26T11:47:40Z) - UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing [28.140945021777878]
We present UniEdit, a tuning-free framework that supports both video motion and appearance editing.
To realize motion editing while preserving source video content, we introduce auxiliary motion-reference and reconstruction branches.
The obtained features are then injected into the main editing path via temporal and spatial self-attention layers.
arXiv Detail & Related papers (2024-02-20T17:52:12Z) - MagicStick: Controllable Video Editing via Control Handle
Transformations [109.26314726025097]
MagicStick is a controllable video editing method that edits the video properties by utilizing the transformation on the extracted internal control signals.
We present experiments on numerous examples within our unified framework.
We also compare with shape-aware text-based editing and handcrafted motion video generation, demonstrating our superior temporal consistency and editing capability than previous works.
arXiv Detail & Related papers (2023-12-05T17:58:06Z) - FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video
editing [65.60744699017202]
We introduce optical flow into the attention module in the diffusion model's U-Net to address the inconsistency issue for text-to-video editing.
Our method, FLATTEN, enforces the patches on the same flow path across different frames to attend to each other in the attention module.
Results on existing text-to-video editing benchmarks show that our proposed method achieves the new state-of-the-art performance.
arXiv Detail & Related papers (2023-10-09T17:59:53Z) - Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image
Diffusion Models [65.268245109828]
Ground-A-Video is a video-to-video translation framework for multi-attribute video editing.
It attains temporally consistent editing of input videos in a training-free manner.
Experiments and applications demonstrate that Ground-A-Video's zero-shot capacity outperforms other baseline methods in terms of edit-accuracy and frame consistency.
arXiv Detail & Related papers (2023-10-02T11:28:37Z) - MagicProp: Diffusion-based Video Editing via Motion-aware Appearance
Propagation [74.32046206403177]
MagicProp disentangles the video editing process into two stages: appearance editing and motion-aware appearance propagation.
In the first stage, MagicProp selects a single frame from the input video and applies image-editing techniques to modify the content and/or style of the frame.
In the second stage, MagicProp employs the edited frame as an appearance reference and generates the remaining frames using an autoregressive rendering approach.
arXiv Detail & Related papers (2023-09-02T11:13:29Z) - StableVideo: Text-driven Consistency-aware Diffusion Video Editing [24.50933856309234]
Diffusion-based methods can generate realistic images and videos, but they struggle to edit existing objects in a video while preserving their appearance over time.
This paper introduces temporal dependency to existing text-driven diffusion models, which allows them to generate consistent appearance for the edited objects.
We build up a text-driven video editing framework based on this mechanism, namely StableVideo, which can achieve consistency-aware video editing.
arXiv Detail & Related papers (2023-08-18T14:39:16Z) - VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing [18.24307442582304]
We introduce VidEdit, a novel method for zero-shot text-based video editing.
Our experiments show that VidEdit outperforms state-of-the-art methods on DAVIS dataset.
arXiv Detail & Related papers (2023-06-14T19:15:49Z) - Edit-A-Video: Single Video Editing with Object-Aware Consistency [49.43316939996227]
We propose a video editing framework given only a pretrained TTI model and a single text, video> pair, which we term Edit-A-Video.
The framework consists of two stages: (1) inflating the 2D model into the 3D model by appending temporal modules tuning and on the source video (2) inverting the source video into the noise and editing with target text prompt and attention map injection.
We present extensive experimental results over various types of text and videos, and demonstrate the superiority of the proposed method compared to baselines in terms of background consistency, text alignment, and video editing quality.
arXiv Detail & Related papers (2023-03-14T14:35:59Z)
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.