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MagicEdit: High-Fidelity and Temporally Coherent Video Editing

Jun Hao Liew, Hanshu Yan, Jianfeng Zhang, Zhongcong Xu, Jiashi Feng

arXiv:2308.14749v1cs.CV

TL;DR

Text-guided video editing still faces challenges in preserving detail across frames, handling large motion, and maintaining per-frame quality. MagicEdit separates content, structure, and motion during training, and the report presents high-fidelity, temporally coherent video editing across several downstream tasks.

  • Problem

    Per-frame editing can lose fine details across frames and struggle with large motion, while per-clip methods can be inefficient or degrade per-frame quality.

    Source: §1 · pp. 1–2

  • Method

    MagicEdit learns video generation conditioned on text and source structure by explicitly disentangling content, structure, and motion during training.

    Source: §2 · pp. 2–3, §1 · p. 2

  • Results

    The report says explicit disentanglement yields high-fidelity, temporally coherent video-to-video translation and supports multiple editing tasks.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The reported applications include video stylization, local editing, video-MagicMix, and video outpainting.

    Source: §4 · p. 3

Abstract

from arXiv · show

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.

1. Introduction

Text-guided video editing seeks to make editing easier, but existing per-frame and per-clip approaches face different quality, consistency, and efficiency challenges. MagicEdit proposes disentangling content, structure, and motion during training to support high-fidelity, temporally consistent editing.

1. Introduction

  • Professional video editing is complex and time-consuming, creating demand for easy-to-use, performant tools.The report notes that editing is also challenging for novice users.Source: §1 · p. 1
  • Per-frame methods can lose fine details and texture across frames and struggle with large motion despite temporal-consistency tricks.The cited strategies include cross-frame attention, flow warping, and latent matching or fusion.Source: §1 · p. 1
  • Per-clip methods reduce temporal inconsistency through explicit motion modeling, but require per-video optimization or can degrade per-frame quality by updating whole networks.The report describes per-video fine-tuning or optimization as inefficient and says whole-network updates can hurt the original text-to-image model’s domain knowledge.Source: §1 · p. 2
  • MagicEdit uses explicit content, structure, and motion disentanglement to target high-fidelity, temporally consistent video-to-video translation.The report presents this training recipe as supporting stylization, local editing, video-MagicMix, and outpainting.Source: §1 · p. 2

2. MagicEdit

MagicEdit edits video by conditioning generation on a text prompt and the source video’s structure. Its training separates content, structure, and motion, then combines the resulting modules for inference.

2. MagicEdit

  • The model learns video generation conditioned on a text prompt and per-frame structure representation to edit content while preserving source structure.The video is represented as F × H × W × 3, with F frames and H and W denoting height and width.Source: §2 · p. 2
  • Stage I trains a base text-to-image diffusion model to make each generated frame adhere to the prompt, using Stable Diffusion v1.5 in this work.The report identifies Stable Diffusion v1.5 as the selected base model.Source: §2 · p. 2
  • Stage II-A trains a structure-conditioned module for frames to follow the source video’s structure or trajectory, using the ControlNet approach.The pretrained UNet parameters are frozen during this stage.Source: §2 · p. 2
  • Stage II-B trains a motion module using temporal transformers to enforce cross-frame consistency while freezing the learned UNet parameters.The report uses vanilla temporal transformers from [5].Source: §2 · p. 2
  • At inference, MagicEdit combines the individually trained modules and can swap in personalized Stable Diffusion weights for different styles and appearance.The base text-to-image weights remain frozen during structure and motion training.Source: §2 · p. 3
  • MagicEdit explicitly disentangles content, structure, and motion during training, keeping the text-to-image UNet frozen while training structure and motion modules.The authors argue that lower-quality, often motion-blurred video data can degrade per-frame quality when all components are jointly modeled; collecting large-scale high-quality video data is described as prohibitively expensive.Source: §2 · p. 3

3. Applications

MagicEdit is presented for stylization, local editing, video concept mixing, and outpainting. The applications use source-video structure or inversion to support different kinds of edits.

3. Applications

  • Stylization extracts source structure, such as disparity or human pose, and uses personalized text-to-image models to change style, subject, or background.The report gives MiDaS and Open-Pose as examples of structure extractors and shows examples in Fig. 3.Source: §3 · p. 3
  • Local editing uses DDIM inversion with a source-content prompt before denoising with the target prompt, to modify selected content while leaving other regions untouched.The report illustrates making a young woman wear glasses as an example.Source: §3 · p. 3
  • Video-MagicMix applies concept mixing to video, with the example of a moving rabbit-alike tiger.The report connects this application to the concept-mixing approach of Liew et al. [6].Source: §3 · p. 3
  • Video outpainting uses inverted latents to keep known regions unchanged, then relaxes that replacement in the final denoising steps for smoother borders.The report shows horizontal, vertical, and large outpainting ratios, including bottom + 100%, and says different prompts can yield different contents.Source: §3 · p. 3

4. Conclusion

The report concludes that disentangling content, structure, and motion during training can yield high-fidelity, temporally coherent video-to-video translation. It presents MagicEdit as a simple training recipe supporting several downstream editing applications.

4. Conclusion

  • The report concludes that explicitly disentangling content, structure, and motion signals during training can produce high-fidelity, temporally coherent video-to-video translation.It presents MagicEdit as a simple recipe for training a video editing tool.Source: §4 · p. 3
  • The reported downstream applications include video stylization, local editing, video-MagicMix, and video outpainting.Source: §4 · p. 3
  • The report frames disentangled training as the basis of MagicEdit’s effective video-editing recipe.Source: §4 · p. 3

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