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MagicEdit: High-Fidelity and Temporally Coherent Video Editing

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arxiv 2308.14749 v1 pith:3LADNHXE submitted 2023-08-28 cs.CV

Jun Hao Liew , Hanshu Yan , Jianfeng Zhang , Zhongcong Xu , Jiashi Feng This is my paper · ORCID
classification cs.CV
keywords videoeditingmagiceditcoherenthigh-fidelitytemporallyachievedappearance
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rethinking Position Embedding as a Context Controller for Multi-Reference and Multi-Shot Video Generation

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  2. UniVideo: Unified Understanding, Generation, and Editing for Videos

    cs.CV 2025-10 conditional novelty 6.0 of 10

    UniVideo combines a frozen MLLM and a video DiT to unify video understanding, generation, in-context editing, visual prompting, and zero-shot free-form video edits under one instruction interface.

  3. UNIC: Unified In-Context Video Editing

    cs.CV 2025-06 conditional novelty 6.0 of 10

    One diffusion transformer handles ID insert, swap, delete, stylization, propagation, and re-camera control in a single model using in-context token concatenation with task-aware positional encoding and bias.

  4. Instruction-based Image Editing: A Survey on Data, Models, Evaluation, and Applications

    cs.CV 2026-07 conditional novelty 4.0 of 10

    A survey of instruction-based image editing plus a new 21-task benchmark, CDD-IIE, on which ten open models are scored by human experts.

  5. A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation

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    Fine-tuning DepthAnythingV2 with a Gaussian negative log-likelihood loss yields the most reliable pixel-wise uncertainty estimates on indoor, street, and object scenes, but it fails on aerial large-depth data.

  6. Survey on Monocular Metric Depth Estimation

    cs.CV 2025-01 unverdicted novelty 1.0 of 10

    A survey of monocular metric depth estimation methods, datasets, and open challenges, with no new experimental results.

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