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StableVideo: Text-driven Consistency-aware Diffusion Video Editing

Wenhao Chai, Xun Guo, Gaoang Wang, Yan Lu

arXiv:2308.09592v1cs.CV

TL;DR

Diffusion models struggle to edit existing video objects while preserving their appearance over time. StableVideo introduces inter-frame propagation and layered atlas aggregation within a text-driven diffusion framework, and experiments report superior qualitative and quantitative results over state-of-the-art methods. The approach remains constrained by NLA failures on substantially deforming non-rigid objects and by diffusion-model limitations in some human or animal scenarios.

  • Problem

    Natural video editing requires edited objects to retain faithful geometry and appearance throughout changing frames, a challenge for diffusion-based methods.

  • Method

    StableVideo uses inter-frame propagation and an atlas aggregation network to generate temporally consistent foreground and background edits with existing diffusion models.

  • Results

    StableVideo achieves superior qualitative and quantitative editing results compared with state-of-the-art methods.

  • Takeaways & Limitations

    The framework provides consistency-aware text-driven editing across diverse natural-video editing scenarios, including compositing, background replacement, and style transfer.

  • Takeaways & Limitations

    NLA may fail on non-rigid objects with significant structural deformation, while diffusion models may struggle with specific human or animal scenarios.

Abstract

from arXiv · show

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 prevents diffusion models from being applied to natural video editing in practical scenarios. In this paper, we tackle this problem by introducing temporal dependency to existing text-driven diffusion models, which allows them to generate consistent appearance for the edited objects. Specifically, we develop a novel inter-frame propagation mechanism for diffusion video editing, which leverages the concept of layered representations to propagate the appearance information from one frame to the next. We then build up a text-driven video editing framework based on this mechanism, namely StableVideo, which can achieve consistency-aware video editing. Extensive experiments demonstrate the strong editing capability of our approach. Compared with state-of-the-art video editing methods, our approach shows superior qualitative and quantitative results. Our code is available at \href{https://github.com/rese1f/StableVideo}{this https URL}.

1 Zhejiang University 2 Microsoft Research Asia

StableVideo edits a video with long-range motion and changing viewpoints while preserving the edited SUV’s geometric and temporal consistency.

  • The method edits foreground and background according to the prompt “Orange SUV in sunny snow winter.”
  • The edited orange SUV maintains high geometric and temporal consistency despite continuously changing viewpoints.

1. Introduction

Natural video editing requires both faithful generation and temporal propagation, but diffusion models struggle to preserve edited-object appearance across videos. StableVideo addresses this with inter-frame propagation and layered atlas aggregation, reporting superior editing performance to state-of-the-art methods.

  • Natural video editing must preserve target-object geometry and generate high-fidelity content while propagating edits consistently throughout the video.
  • Diffusion methods achieve strong image-editing results but remain difficult to apply to video because existing-object appearance must persist over time.
  • Atlas-based methods represent semantically corresponding pixels globally, enabling edits to map back to frames with temporal smoothness.
  • Directly editing video atlases can fail because viewpoint and camera movement distort atlas appearance, contaminating corresponding edited frames.
  • StableVideo combines inter-frame propagation for coherent geometry with aggregation-based edited atlases for temporal consistency.
  • The paper reports extensive experiments showing superior editing performance compared with state-of-the-art methods.

2. Related Work

Prior work spans diffusion image editing, video editing, temporal propagation, and layered video representations. StableVideo combines these directions through separate foreground-background atlas editing and propagation mechanisms.

  • 2.1. Diffusion for Image Editing: Diffusion image-editing methods use noise reversal, prompt activation mixing, semantic test-time editing, or image-specific finetuning to control generated content.
  • 2.2. Diffusion for Video Editing: Video editing is harder than image editing because diffusion-based methods must preserve geometric and temporal consistency across frames.
  • 2.3. Temporal Propagation in Video Editing: Temporal propagation methods transfer contents between frames using key frames or optical flow to support temporal consistency.
  • 2.3. Temporal Propagation in Video Editing: StableVideo uses NLA to edit foreground and background separately, combining key-frame editing with inter-frame propagation for consistent reconstruction.
  • 2.3. Temporal Propagation in Video Editing: Layered atlas approaches compress videos into unified 2D representations so edits can be applied globally and mapped back across frames.

3. Method

StableVideo edits foreground and background separately while using inter-frame propagation and atlas aggregation to maintain appearance, geometry, and temporal consistency. Key-frame editing, layered representations, and diffusion-based generation form the core pipeline.

  • Pipeline: StableVideo uses NLA to separate foreground and background editing, map edited key frames into partial atlases, and aggregate them into edited foreground atlases.The framework can also handle multiple foreground layers.
  • Pipeline: The method fixes foreground and background UV mappings, generates edited atlases with guided latent diffusion models, and reconstructs the video frame by frame.Foreground and background are composited using opacity-aware mappings.
  • Key-frame editing: Key frames are selected with overlap between adjacent frames and coverage of all object faces, then the first frame is edited using a text prompt and structure guidance.Subsequent key frames receive propagated appearance information from the preceding edited frame.
  • Experiments: Experiments cover compositing and background replacement, with results showing high-quality editing, semantic matching, geometric consistency, and temporal continuity across demonstrated videos.The compositing examples include non-rigid and rigid foreground objects such as a bear and a car.
  • Inter-frame propagation: Inter-frame propagation maps previous-frame appearance into the current frame, encodes the partial appearance, adds VP-SDE noise, and denoises under text and structure conditions.The mechanism uses current-frame structure and previous-frame appearance to support geometric consistency without model training or fine-tuning.
  • Aggregation network: An aggregation network combines partial atlases generated from edited key frames, while editing frames rather than atlases provides more viewpoint information and reduces atlas-to-frame mapping failures.The paper describes the aggregation network as a simple two-layer 2D convolution network and illustrates its training process with sampled key frames.

4. Experiments

StableVideo is evaluated across multiple video-editing scenarios, comparisons with prior methods, consistency metrics, and ablations of its key components. The experiments report diverse editing capabilities, improved consistency, and a trade-off between realism and fidelity controlled by t0.

  • Editing Results: StableVideo supports compositing, background replacement, and style transfer while maintaining temporal consistency.Background replacement also preserves foreground depth geometry and temporal continuity of perspective.
  • Comparison to Prior Arts: Against Text2LIVE and Tune-A-Video, StableVideo produces more holistic or consistent foreground edits and offers higher quality with faster inference than Text2LIVE.Tune-A-Video captures text semantics but struggles with video layout and object geometry consistency.
  • Consistency Analysis: StableVideo achieves comparable CLIP score with much lower frame deviations, indicating effective and stable editing.The paper evaluates motion consistency using dense optical-flow deviations and deviation consistency between edited frames.
  • Ablation Study: Direct atlas editing can cause obvious deformation because atlas distortions significantly affect diffusion models.This motivates editing key frames rather than modifying the atlas directly.
  • Ablation Study: Inter-frame propagation maintains foreground geometry across key-frame edits, whereas random generation produces significant differences among generated key frames.The ablation compares random generation and fixed-initial-latent settings for key-frame editing.
  • Ablation Study: At t0 around 0.8, inter-frame propagation balances increasing realism against decreasing appearance fidelity to the previous frame.Larger t0 values make results more realistic but progressively reduce their match with the previous frame.

5. Limitations and Future Works

StableVideo is constrained by NLA on non-rigid objects and by diffusion-model limitations in scenarios such as human or animal editing.

  • Scope Constraints: NLA may fail for non-rigid objects with significant structural deformation, although splitting long videos into short clips can mitigate this limitation.The authors note that this strategy remains unable to address every case.
  • Scope Constraints: StableVideo is also constrained by diffusion models that may struggle with specific scenarios such as editing humans or animals.The authors suggest optimizing the diffusion model to align generated contents with reconstructed ones.

6. Conclusion

The paper proposes StableVideo, a text-driven diffusion video-editing approach designed to address consistency in foreground object editing.

  • Conclusion: StableVideo combines an inter-frame propagation mechanism with an atlas aggregation network to improve consistency in text-driven video editing.The paper reports superior qualitative and quantitative results compared with state-of-the-art approaches.
  • Conclusion: Experiments demonstrate superior qualitative and quantitative results compared with state-of-the-art video-editing approaches.The conclusion summarizes the method's demonstrated editing performance.

A. Implementation Details

Implementation uses sampled key frames, a lightweight trained aggregation network, and Canny-based structure guidance, with failure cases arising from imperfect diffusion control and NLA reconstruction.

  • Implementation Details: Key frames are selected every 20 frames, and the aggregation network is trained for 500 epochs with learning rate 0.003 and momentum 0.9.The network has two convolution layers with a ReLU and is trained once per edit at inference.
  • Failure Cases: Existing pre-trained diffusion models can cause partial-editing failures despite preserving target-object structure across time.User-provided masks are proposed as a future way to handle this issue.
  • Failure Cases: NLA failures caused by complex motion or occlusion can also make editing fail, although key-frame editing and partial atlases can alleviate the problem.The limitation arises when NLA cannot build the foreground atlas reliably.

C. Complexity Analysis

StableVideo is presented as more efficient in practical application than Text2LIVE and Tune-A-Video, while an example shows a limitation in partial editing. The reported inference comparison uses a 768 × 432 video with 70 frames.

  • Failure case: In partial editing, such as changing a skirt’s color, the diffusion models may generate the whole person instead.This is presented as an example of failure editing.
  • Efficiency: StableVideo requires no edit training and about ∼30 sec for edit inference, compared with ∼1 hours plus ∼10 sec for Text2LIVE and ∼4 min for Tune-A-Video.The comparison reports approximately ∼10 hr of video training for Text2LIVE and StableVideo, while Tune-A-Video has no listed video-training time.
  • Efficiency: The inference-speed comparison was tested on a 768 × 432 video with 70 frames using a single NVIDIA A40.For StableVideo, three key frames were selected for foreground editing.
  • Efficiency: StableVideo performs lightweight atlas-aggregation training at inference, which the paper describes as more efficient for practical application than Text2LIVE and Tune-A-Video.The cited comparison is specifically with Tune-A-Video and Text2LIVE.

D. More Editing Results

StableVideo preserves temporal consistency and geometry in foreground editing, while composite edits use semantically correlated prompts for the foreground and background. The shared diffusion model also produces semantically consistent composite results.

  • Composite editing: Because the foreground and background use the same diffusion model, composite edits are highly semantically consistent while preserving geometry across time.The paper reports both semantic consistency and temporal geometry preservation for these results.
  • Foreground editing: StableVideo preserves temporal consistency when editing a boat video with relatively complex ship geometry.The result is presented as a foreground-editing example.
  • Composite editing: Composite editing separately modifies the foreground and background with semantically correlated prompts.The composite-editing setup is described in Figure C.
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