Pith. sign in

REVIEW 9 cited by

MagicEdit: High-Fidelity and Temporally Coherent Video Editing

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

MagicEdit: High-Fidelity and Temporally Coherent Video Editing

classification cs.CV
keywords videoeditingmagiceditcoherenthigh-fidelitytemporallyachievedappearance
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original 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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 9 Pith papers

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

  1. VACE: All-in-One Video Creation and Editing

    cs.CV 2025-03 unverdicted novelty 7.0

    VACE unifies reference-to-video generation, video-to-video editing, and masked video-to-video editing in one Diffusion Transformer framework using a Video Condition Unit for inputs and a Context Adapter for task injection.

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

    cs.CV 2026-04 conditional novelty 6.0

    SideInfo-RoPE encodes reference-identity agreement as an extra rotary axis, disambiguating similar characters in multi-reference multi-shot video generation while keeping full semantic attention.

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

    cs.CV 2025-10 conditional novelty 6.0

    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.

  4. VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness

    cs.CV 2025-03 accept novelty 6.0

    VBench-2.0 is a benchmark suite that automatically evaluates video generative models on five dimensions of intrinsic faithfulness: Human Fidelity, Controllability, Creativity, Physics, and Commonsense using VLMs, LLMs...

  5. Depth Anything V2

    cs.CV 2024-06 unverdicted novelty 6.0

    Depth Anything V2 delivers finer, more robust monocular depth predictions by replacing real labeled images with synthetic data, scaling the teacher model, and using large-scale pseudo-labeled real images for student training.

  6. VideoPoet: A Large Language Model for Zero-Shot Video Generation

    cs.CV 2023-12 unverdicted novelty 6.0

    VideoPoet is a large language model that performs zero-shot video generation with audio from diverse multimodal conditioning signals.

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

    cs.CV 2026-07 conditional novelty 4.0

    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.

  8. Evolution of Video Generative Foundations

    cs.CV 2026-04 unverdicted novelty 2.0

    This survey traces video generation technology from GANs to diffusion models and then to autoregressive and multimodal approaches while analyzing principles, strengths, and future trends.

  9. Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

    cs.CV 2024-02 unverdicted novelty 2.0

    The paper reviews the background, technology, applications, limitations, and future directions of OpenAI's Sora text-to-video generative model based on public information.