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arxiv: 2606.26740 · v1 · pith:Q3SFXGZQnew · submitted 2026-06-25 · 💻 cs.CV

LiveEdit: Towards Real-Time Diffusion-Based Streaming Video Editing

Xinyu Wang , Chongbo Zhao , Fangneng Zhan , Yue Ma This is my paper

Pith reviewed 2026-06-26 05:24 UTC · model grok-4.3

classification 💻 cs.CV
keywords streaming video editingdiffusion modelsreal-time video editingvideo distillationcausal video generationaugmented reality editingmask caching
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The pith

A three-stage distillation pipeline converts bidirectional video diffusion models into causal real-time editors that preserve content at 12.66 frames per second.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents LiveEdit as a streaming video editing system that performs frame-by-frame causal edits while maintaining stable backgrounds and non-edited regions over long sequences. It addresses the gap between powerful but slow bidirectional diffusion models and the need for low-latency unidirectional processing in interactive settings. The approach relies on progressively transferring editing ability through distillation and adding a mask cache to reuse computations across frames. A sympathetic reader would care because existing streaming methods either sacrifice visual stability or cannot run in real time, limiting use in augmented reality or live applications. If the method works as described, it opens practical deployment of diffusion-based editing at interactive speeds.

Core claim

The central claim is that a three-stage distillation pipeline can transfer editing capability from a powerful bidirectional foundation model to an efficient unidirectional streaming editor while preserving stable long-horizon content and visual fidelity, and that an AR-oriented mask cache reuses region-related computation to accelerate inference to 12.66 FPS, achieving state-of-the-art visual quality among streaming baselines on a new dedicated benchmark.

What carries the argument

The three-stage distillation pipeline that progressively transfers editing capability from a bidirectional foundation model to a causal unidirectional editor, supported by an AR-oriented mask cache that reuses region-related computation across frames.

If this is right

  • Real-time causal editing becomes feasible for interactive and augmented reality applications without sacrificing visual quality.
  • Stable content preservation holds across extended video horizons without post-hoc adjustments.
  • A dedicated benchmark enables standardized comparison of future streaming video editing methods.
  • Inference speed reaches levels that support frame-by-frame responsiveness in live scenarios.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same distillation strategy could be tested on other region-controlled diffusion tasks such as inpainting or object insertion in video.
  • The mask cache mechanism might reduce redundant work in any frame-sequential video processing pipeline that relies on spatial masks.
  • Extending the unidirectional editor to handle variable frame rates or higher resolutions would be a direct next measurement of the approach's robustness.

Load-bearing premise

The distillation process can move editing skills from the bidirectional model to the unidirectional one while keeping long-term stability and fidelity without any extra post-processing steps.

What would settle it

Long video sequences edited with the distilled model show noticeable background drift or unintended changes in preserved regions after dozens of frames, even when the mask cache is active.

Figures

Figures reproduced from arXiv: 2606.26740 by Chongbo Zhao, Fangneng Zhan, Xinyu Wang, Yue Ma.

Figure 1
Figure 1. Figure 1: Gallery of various editing results and efficiency comparisons. We propose the LiveEdit, a novel streaming video editing framework capable of performing causal, chunk-by-chunk manipulation with ultra-low latency and strict background preservation. By synergizing a progressive three-stage architectural distillation pipeline with an AR-oriented Mask Cache, LiveEdit effectively resolves the architectural incom… view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of video editing paradigms. Unlike bidi￾rectional models that suffer from inefficient inference, and past streaming models that fail to preserve accurate unedited content, our proposed streaming editing model leverages a Causal DiT with a mask-guided cache mechanism to achieve high-fidelity and effi￾cient editing. nisms and text embeddings, the model learns complex edit￾ing mappings supervised b… view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of the attention distribution shift. Left: The bidirectional prior exhibits localized attention gather￾ing. Right: Direct causal truncation forces attention to spread uniformly across all historical frames. We summarize the two primary observations regarding the inefficiencies of adapting state-of-the-art video diffusion models to the streaming video editing task and propose the modules to ad… view at source ↗
Figure 4
Figure 4. Figure 4: Overview of the proposed streaming video editing framework. Our approach features a three-stage distillation pipeline that transfers editing capabilities from a bidirectional DiT to a 4-step causal model. Furthermore, an AR-oriented Mask Cache accelerates real-time inference by dynamically decoupling computation and reusing tokens in unedited background regions. causal attention. Let Mcausal denote the cau… view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of the temporal consistency analysis and mask generation process. The left panels show (from top to bottom) the source video frames, the synthesized video frames, the computed difference matrices, and the resulting binary masks. The right panels display the statistical distributions of Temporal IoU and Pixel Difference across the sequence, with mean values of 0.016% and 0.126%, respectively, … view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison of streaming video editing performance. The source videos and instructions are displayed at the top. While existing methods exhibit significant limitations, leading to structural collapse or an inability to accurately follow the text, our approach precisely modifies the target regions and preserves the visual quality and temporal coherence of the original scenes. 4.3. Quantitative co… view at source ↗
Figure 7
Figure 7. Figure 7: Visual comparison of different cache locations. With the instruction ”Change the red currants to deep purple grapes with a thin layer of frost on their skins” [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of token cosine similarity between consec [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 1
Figure 1. Figure 1: User study results. Volunteers ranked the generated videos from our method and six baselines across three metrics: Instruction Consistency, Background Preservation, and Overall Quality. The line plots indicate the proportion of top-3 selections. Our proposed approach overwhelmingly dominates the evaluations, securing the vast majority of absolute ”Best” rankings across all dimensions.. LucyEdit also exhibi… view at source ↗
Figure 2
Figure 2. Figure 2: More cases generated by LiveEdit [PITH_FULL_IMAGE:figures/full_fig_p017_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: More cases generated by LiveEdit [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: More cases generated by LiveEdit [PITH_FULL_IMAGE:figures/full_fig_p019_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: More comparison between baseline and our LiveEdit [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: More comparison between baseline and our LiveEdit [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
read the original abstract

Streaming video editing has made rapid progress, yet practical deployment is still limited by two core issues: maintaining stable backgrounds and non-edited regions over time, and achieving the low latency required for real-time interactive scenarios. Meanwhile, recent streaming video generation methods are mostly developed for synthesis and cannot be directly applied to editing due to the strict preservation requirement and region-specific control. In this work, we present a novel streaming video editing framework that performs causal, frame-by-frame editing with strong content preservation and real-time responsiveness. Our key design is a three-stage distillation pipeline that progressively transfers editing capability from a powerful bidirectional foundation model to an efficient unidirectional streaming editor, enabling stable long-horizon edits without sacrificing visual fidelity. To further support real-time deployment, we introduce an AR-oriented mask cache that reuses region-related computation across frames, substantially reducing redundant processing and accelerating inference. Finally, we establish a dedicated benchmark for streaming video editing. Extensive evaluations demonstrate that our method achieves state-of-the-art visual quality among streaming baselines while drastically boosting inference speed to 12.66 FPS, making it suitable for interactive and augmented reality applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents LiveEdit, a streaming video editing framework that performs causal frame-by-frame editing via a three-stage distillation pipeline transferring capabilities from a bidirectional foundation model to a unidirectional causal editor, augmented by an AR-oriented mask cache for reduced redundant computation, and introduces a dedicated benchmark. It claims SOTA visual quality among streaming baselines at 12.66 FPS for interactive and AR applications.

Significance. If the distillation successfully transfers long-horizon stability and the speed/quality claims are validated with proper metrics, the work would address key barriers to practical real-time video editing, enabling deployment in interactive and augmented reality scenarios while providing a new benchmark to support further research.

major comments (2)
  1. [Abstract] Abstract: The claim that the three-stage distillation pipeline transfers editing capability from a bidirectional model to a causal unidirectional editor while preserving stable long-horizon content and visual fidelity without post-hoc adjustments is load-bearing for the central contribution, yet the text supplies no per-stage objectives, loss terms, or mechanisms that would enforce temporal consistency once future-frame context is removed.
  2. [Abstract] Abstract: The assertion of state-of-the-art visual quality and 12.66 FPS is presented without any quantitative metrics, baselines, error bars, dataset details, or evaluation protocol, rendering the primary empirical claims unverifiable from the provided manuscript text.
minor comments (1)
  1. [Abstract] The abstract would benefit from a brief parenthetical note on the benchmark construction or evaluation protocol to allow readers to assess the scope of the SOTA claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that the three-stage distillation pipeline transfers editing capability from a bidirectional model to a causal unidirectional editor while preserving stable long-horizon content and visual fidelity without post-hoc adjustments is load-bearing for the central contribution, yet the text supplies no per-stage objectives, loss terms, or mechanisms that would enforce temporal consistency once future-frame context is removed.

    Authors: The abstract is a concise summary. The per-stage objectives, loss terms (reconstruction, temporal consistency, and distillation losses), and mechanisms for long-horizon stability (unidirectional causal attention plus mask caching) are detailed in Section 3 of the full manuscript. We will revise the abstract to briefly reference these elements for improved self-containment. revision: yes

  2. Referee: [Abstract] Abstract: The assertion of state-of-the-art visual quality and 12.66 FPS is presented without any quantitative metrics, baselines, error bars, dataset details, or evaluation protocol, rendering the primary empirical claims unverifiable from the provided manuscript text.

    Authors: The abstract summarizes results; full quantitative details (metrics such as PSNR/SSIM/LPIPS, baselines, error bars, datasets, and protocol) appear in Section 4 with tables and figures. The reported 12.66 FPS is the measured speed. We will revise the abstract to include one or two key metrics while respecting length limits. revision: yes

Circularity Check

0 steps flagged

No circularity; derivation self-contained against external benchmarks

full rationale

The abstract and provided text describe a three-stage distillation pipeline transferring editing capability from a bidirectional model to a causal editor, plus an AR-oriented mask cache, with claims evaluated on a new benchmark and reported FPS. No equations, fitted parameters renamed as predictions, self-citations used as load-bearing uniqueness theorems, or ansatzes smuggled via prior work are present. The central claims rest on empirical evaluations and design choices that do not reduce by construction to the inputs; the pipeline is presented as an independent engineering contribution rather than a mathematical identity. This matches the default expectation of no significant circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no free parameters, axioms, or invented entities are extractable.

pith-pipeline@v0.9.1-grok · 5727 in / 1001 out tokens · 20904 ms · 2026-06-26T05:24:18.762275+00:00 · methodology

discussion (0)

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Reference graph

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