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Edit-A-Video: Single Video Editing with Object-Aware Consistency

Chaehun Shin, Heeseung Kim, Che Hyun Lee, Sang-gil Lee, Sungroh Yoon

arXiv:2303.07945v4cs.CV

TL;DR

Prior text-guided video-editing approaches have limitations in controlling edited regions over time, while accurate handling of target objects and backgrounds remains challenging. Edit-A-Video adapts a pretrained TTI model using a single source video and adds temporally consistent blending; the authors report favorable baseline comparisons in preference, consistency, and text alignment.

  • Problem

    Prior video-editing methods either edit unwanted areas or calculate per-frame edit regions without temporal information, while handling target objects and backgrounds remains challenging.

    Source: §1 · p. 2

  • Method

    Edit-A-Video inflates and tunes a pretrained 2D TTI model on one source video, then edits via inversion and source-to-target attention injection, with TC Blending for consistent masks.

    Source: §1 · pp. 2–3

  • Results

    Across varied videos and prompts, the authors report it as most favorable versus baselines in human preference, with automatic metrics showing improved consistency and text alignment.

    Source: §1 · p. 3

  • Takeaways & Limitations

    The authors anticipate a simple, intuitive video-editing method that uses a single <text, video> pair and a pretrained TTI model.

    Source: §5 · p. 14

Abstract

from arXiv · show

Despite the fact that text-to-video (TTV) model has recently achieved remarkable success, there have been few approaches on TTV for its extension to video editing. Motivated by approaches on TTV models adapting from diffusion-based text-to-image (TTI) models, we suggest the video editing framework given only a pretrained TTI model and a single <text, video> pair, which we term Edit-A-Video. The framework consists of two stages: (1) inflating the 2D model into the 3D model by appending temporal modules and tuning on the source video (2) inverting the source video into the noise and editing with target text prompt and attention map injection. Each stage enables the temporal modeling and preservation of semantic attributes of the source video. One of the key challenges for video editing include a background inconsistency problem, where the regions not included for the edit suffer from undesirable and inconsistent temporal alterations. To mitigate this issue, we also introduce a novel mask blending method, termed as sparse-causal blending (SC Blending). We improve previous mask blending methods to reflect the temporal consistency so that the area where the editing is applied exhibits smooth transition while also achieving spatio-temporal consistency of the unedited regions. We present extensive experimental results over various types of text and videos, and demonstrate the superiority of the proposed method compared to baselines in terms of background consistency, text alignment, and video editing quality.

1. Introduction

Edit-A-Video extends a pretrained text-to-image model for one-shot text-guided video editing, with temporal modeling and source-to-target attention injection. It also targets abrupt background changes with TC Blending, and the authors report favorable comparisons with baselines.

1. Introduction

  • Edit-A-Video inflates a pretrained 2D TTI model into a 3D model, tunes it on one source video, then edits through inversion and source-to-target attention-map injection.The framework combines a pretrained text-to-image model with video-editing techniques.Source: §1 · pp. 2–3
  • Abrupt temporal changes in unedited backgrounds degrade video quality; TC Blending generates a sharp, spatio-temporally consistent mask approximating the source video's edit region.The method extends a spatial local-blending technique for still images.Source: §1 · pp. 2–3
  • The authors report Edit-A-Video as the most favorable method versus baselines in human preference, with automatic metrics showing improved consistency and text alignment.Ablation studies also support TC Blending’s effectiveness for consistency and overall editing quality.Source: §1 · p. 3
  • The study analyzes how injecting each attention map affects spatio-temporal consistency in the framework.Source: §1 · p. 3

2. Background

The background traces diffusion sampling and image-editing methods before turning to video-specific temporal-consistency challenges. It contrasts prior video-editing strategies with Edit-A-Video’s temporal modeling and mask blending.

2.1. Denoising Diffusion Probabilistic Models (DDPM)

  • DDPM gradually noises data toward Gaussian noise and models a Gaussian reverse transition; this paper follows DDIM’s deterministic, faster sampling formulation.The reverse transition network approximates an intractable reverse-process term.Source: §2.1 · pp. 3–4

2.2. Tune-A-Video

  • Tune-A-Video inflates 2D convolutions, adds temporal attention, and limits sparse spatio-temporal attention to the first and previous frames for linear frame-count cost.It tunes temporal, sparse spatio-temporal, and text-video cross-attention layers to generate temporally coherent videos.Source: §2.2 · p. 4
  • Although Tune-A-Video generates temporally consistent videos, it modifies entire frames, so background content may not remain faithful to the source.Edit-A-Video instead aims to preserve attributes unrelated to the target text.Source: §2.2 · p. 4

2.3. Real Image Editing with Null-Text Inversion

  • Prompt-to-Prompt injects source-image cross-attention maps during target-text generation, while Null-Text Inversion optimizes null-text embeddings to preserve reconstruction under guidance.Prompt-to-Prompt can be combined with Null-Text Inversion for real-image editing.Source: §2.3 · pp. 4–5
  • Image-editing methods cannot transfer directly to video because video editing requires temporal consistency; the proposed framework adds temporal attention and TC Blending.It also uses sparse spatio-temporal attention on top of Null-Text Inversion and Prompt-to-Prompt.Source: §2.3 · p. 5

2.4. Text-Guided Video Editing

  • Prior video-editing approaches use large paired datasets or time-consuming neural layered atlases, while TTI-based attention-mask methods lack inter-frame temporal information.The latter can produce temporally inconsistent frame-wise masks and undesirable artifacts.Source: §2.4 · p. 5
  • Edit-A-Video inflates a 2D TTI model with temporal modules, inverts the source video, and edits through attention-map injection and TC Blending.TC Blending uses sparse spatio-temporal attention to extract sharp, frame-consistent masks.Source: §2.4 · p. 6

3. Method

Edit-A-Video adapts a pretrained image model for single-video editing, then uses attention-map injection and temporal-consistent blending to preserve video structure. Its method also examines how the duration of three attention types affects editing.

3. Method

  • Edit-A-Video inflates a pretrained text-to-image model into a video model, then edits an inverted source video by injecting its attention maps during denoising.The framework analyzes the roles of the inflated model’s attention modules.Source: §3 · p. 6, §3.1 · pp. 6–7

3.1. Framework

  • The framework identifies background inconsistency as unwanted regions changing abruptly across frames during editing.The issue is associated with spatial blending masks that lack temporal modeling.Source: §3.1 · p. 7, §3.2 · p. 7

3.2. Temporal-Consistent Blending

  • TC Blending uses sparse spatio-temporal attention between the current frame and the first and previous frames to create a sharp, temporally consistent editing mask.The first-frame interaction maintains the target object in each mask, while the previous-frame interaction smooths mask transitions.Source: §3.2 · pp. 8–9

3.3. Hyperparameters for Editing

  • Attention-injection durations are set to 0.2 for cross-attention, 0.8 for temporal attention, and 0.5 for sparse spatio-temporal attention.Cross-attention balances source layout and target semantics; varying temporal-attention duration has little editing-quality impact, while sparse spatio-temporal attention balances preserving source dynamics with editing the target object.Source: §3.3 · pp. 9–10

4. Experiments

Edit-A-Video is compared with three baselines using human ratings, automatic metrics, and qualitative examples. The reported comparisons favor Edit-A-Video, while ablations examine the effects of TC Blending and its threshold.

4.1. Implementation Details

  • The evaluation covers 100 text-video pairs, with 62 users rating overall quality and automatic measures assessing text alignment and background preservation.The pairs include 25 videos with four captions each; the automatic metrics are CLIP similarity, LPIPS, and PSNR.Source: §4.1 · p. 10

4.2. Baseline Comparisons

  • Edit-A-Video significantly outperformed the baselines in user evaluation and exceeded Video-P2P on all reported metrics.The paper also reports that Tune-A-Video reflects target text but alters backgrounds, while SDEdit preserves source content but has the lowest text alignment.Source: Figure 5 · p. 11, §4.2 · p. 12
  • Qualitative examples show Tune-A-Video changing background content, SDEdit missing target text, and Video-P2P altering regions that should remain unchanged.Edit-A-Video is reported to preserve unedited properties while aligning with the target prompt.Source: §4.2 · p. 12

4.3. Ablation

  • With TC Blending, Edit-A-Video achieves higher user scores than the version without it, and the reported metrics and qualitative examples support improved masking and background preservation.The user-score difference is statistically significant (p-value < 0.01); Table 2 reports overall quality, non-target preservation, and mask IoU.Source: Table 2 · p. 12, §4.3 · p. 13
  • Without TC Blending, changing threshold τ fails to capture sharp object regions and can produce abrupt frame-wise mask changes and artifacts; a properly set threshold yields sharp, smooth masks.The authors conclude that TC Blending mitigates background inconsistency.Source: §4.3 · pp. 13–14

5. Conclusion

Edit-A-Video adapts a pretrained text-to-image model to a single source video, then edits through inversion and attention-map injection. The conclusion presents temporal-consistent blending as a means of preserving content and coherence, and reports superior performance to baselines across various aspects.

5. Conclusion

  • The framework uses a pretrained TTI model and a single text-video pair, inflating and tuning the model on the source video for temporal modeling.Editing uses inversion and attention-map injection with a target prompt.Source: §5 · p. 14
  • Temporal-consistent blending uses the model’s temporal modeling capability to support content preservation and temporal coherence.Source: §5 · p. 14
  • The authors report superior performance to baselines in various aspects and anticipate a simple, intuitive video-editing method.Source: §5 · p. 14

Appendix A. Human Preference Study

The appendix describes two human-preference studies: one rates overall editing quality, and the other assesses TC Blending’s background-preservation effect.

Appendix A. Human Preference Study

  • In the overall-quality study, 62 participants rate edited videos on a five-point scale.Source: Appendix A · p. 17
  • Overall ratings consider background preservation, text alignment, and video realism, including visual quality and smoothness.Source: Appendix A · p. 17
  • A second study asks 49 participants to rate background preservation with and without TC Blending.Source: Appendix A · p. 17

Appendix B. Additional Samples

The appendix supplies additional examples of Edit-A-Video outputs, baseline comparisons, and attention-injection hyperparameter settings.

Appendix B. Additional Samples

  • Additional examples include object substitutions, style changes, and scene changes across several source videos.Listed prompts include changing a man playing guitar to a bear or monkey, changing a cow’s setting, and altering a runner’s appearance.Source: Appendix B · pp. 17–19
  • Figure C provides additional generated-sample comparisons between Edit-A-Video and baselines.Source: Appendix B · p. 17, Figure C · p. 20
  • Figure D shows samples associated with adjustments to attention-injection hyperparameters.Source: Appendix B · p. 17, Figure D · p. 21

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