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EVE: Efficient zero-shot text-based Video Editing with Depth Map Guidance and Temporal Consistency Constraints

Yutao Chen, Xingning Dong, Tian Gan, Chunluan Zhou, Ming Yang, Qingpei Guo

arXiv:2308.10648v1cs.CVcs.AI

TL;DR

Text-based video editing faces a trade-off between fine-tuning cost and generation performance, with temporal consistency a key challenge. EVE uses depth maps and temporal constraints in a zero-shot editor, and the authors report a satisfactory performance-efficiency trade-off while introducing ZVE-50 for fair comparisons.

  • Problem

    Text-based video editing faces high fine-tuning costs and limited generation capacity, while zero-shot methods may suffer from spatio-temporal distortion and inconsistency.

    Source: §1 · p. 2

  • Method

    EVE is a zero-shot video editor that uses depth-map guidance and temporal consistency constraints, and updates target latent features without fine-tuning the diffusion model.

    Source: §1 · p. 2

  • Results

    The authors report that EVE achieves a satisfactory trade-off between performance and efficiency, and construct and benchmark ZVE-50 for fair comparison.

    Source: §1 · p. 2, §5 · p. 7

  • Takeaways & Limitations

    ZVE-50 is presented as a public benchmark for fair comparison in video editing, and the authors say it can facilitate future research.

    Source: §1 · p. 2, §5 · p. 7

    The authors identify a remaining performance gap between zero-shot and tuning-based video editing methods and aim to further improve edited-video quality.

    Source: §6 · p. 7

Abstract

from arXiv · show

Motivated by the superior performance of image diffusion models, more and more researchers strive to extend these models to the text-based video editing task. Nevertheless, current video editing tasks mainly suffer from the dilemma between the high fine-tuning cost and the limited generation capacity. Compared with images, we conjecture that videos necessitate more constraints to preserve the temporal consistency during editing. Towards this end, we propose EVE, a robust and efficient zero-shot video editing method. Under the guidance of depth maps and temporal consistency constraints, EVE derives satisfactory video editing results with an affordable computational and time cost. Moreover, recognizing the absence of a publicly available video editing dataset for fair comparisons, we construct a new benchmark ZVE-50 dataset. Through comprehensive experimentation, we validate that EVE could achieve a satisfactory trade-off between performance and efficiency. We will release our dataset and codebase to facilitate future researchers.

1. Introduction

EVE targets the trade-off between costly fine-tuning and limited generation performance in text-based video editing, where preserving temporal consistency is a central challenge. It adds depth-map guidance and temporal constraints, and introduces ZVE-50 for fair comparison.

1. Introduction

  • Existing video-editing methods face a trade-off between the considerable fine-tuning cost of tuning-based approaches and the unsatisfactory generation performance of current methods.Zero-shot approaches avoid time-consuming fine-tuning, but may exhibit spatio-temporal distortion and inconsistency.Source: §1 · p. 2
  • Editing video with image diffusion models requires temporal constraints because edited frames can otherwise exhibit spatio-temporal distortion and inconsistency.The authors argue that video editing must account for frames comprehensively, unlike image editing.Source: §1 · p. 2
  • EVE introduces depth-map guidance and frame-aligned attention to reinforce temporal consistency during zero-shot video editing.Depth maps provide spatial-layout and motion cues, while frame-aligned attention directs attention to previous and current frames.Source: §1 · p. 2
  • EVE updates target latent features without fine-tuning the complex diffusion model, taking about 83.1 seconds on average to edit an 8-frame video.The optimization strategy is designed to incorporate depth maps into the noise-to-image inference procedure.Source: §1 · p. 2
  • ZVE-50 is presented as the first dataset for zero-shot text-based video editing and is intended to support fair comparisons.The paper notes a lack of public video-editing datasets for fair performance comparison.Source: §1 · p. 2
  • Experiments on ZVE-50 indicate that EVE is a robust and efficient zero-shot video editing method.The authors report a satisfactory trade-off between performance and efficiency.Source: §1 · p. 2

2. Related Work

Related work describes diffusion models as strong tools for image synthesis and editing, but notes their training cost. Text-based video editing includes tuning-based and zero-shot approaches, each with distinct costs and consistency challenges.

Diffusion Models

  • Diffusion models [3] [4] [18] have achieved state-of-the-art performance in image synthesis and translation, while training them from scratch is expensive and time-consuming.Source: §2 · p. 3

Text-based Image Editing

  • Text-based image editing [1] [11] [26] manipulates image attributes or styles using text, with DreamBooth [19] relying on fine-tuning and T2I-Adapters [13] offering low training cost.Source: §2 · p. 3

Text-based Video Generation and Editing

  • Video editing [8] [12] includes tuning-based methods [5] and zero-shot methods [15], but FateZero [15] has limited editing performance and relies on costly Tune-A-Video [23].Source: §2 · p. 3

3. Methodology

The methodology frames video editing as producing a prompt-aligned video similar to the original, using latent diffusion and DDIM inversion and denoising. EVE adds depth-map features and frame-aligned attention to constrain the editing process.

3.1. Preliminary: DMs, LDMs, and DDIMs

  • Latent diffusion models denoise learned latent features, while DDIM sampling maps noise to a clean latent and DDIM inversion maps a clean latent to noise.The paper uses DDIM inversion to create a trajectory from the original clean latent to noise, then starts denoising from that noise so the generated video remains close to the original.Source: §3.1 · p. 3

3.2. Problem Formulation

  • Zero-shot text-based video editing aims to produce an edited video that follows prompt P while remaining similar to original video V.Source: §3.2 · p. 3

3.3. Overall Framework

  • EVE extracts frame latents, depth-map features, and prompt features, then applies T-step DDIM inversion to video latents using depth-map features.The depth-map features enter the frozen UNet's down-sampling pass, rectifying discrepancies among neighboring frames at each inversion step.Source: §3.3 · p. 4
  • EVE denoises the inverted noise for T steps to obtain edited video features, adding depth-map guidance and frame-aligned attention to constrain consistency.The attention mechanism places explicit temporal constraints on the edited video.Source: §3.3 · p. 4

4. Parameter Optimization. To reduce the computation

EVE’s temporal-consistency strategies guide video editing with depth maps and frame-aligned attention, while its optimization updates latent noise rather than the frozen networks. The procedure also balances creative and temporally consistent latent estimates.

4. Parameter Optimization. To reduce the computation

  • To reduce computation, EVE freezes feature extractors and UNets and optimizes only the DDIM denoising noise vectors; the resulting latent features are decoded into the edited video.The procedure describes gradient-descent updates and cosine similarity computation.Source: §4 · p. 5
  • The denoising procedure also derives an auxiliary latent without strict depth-map constraints, pairing its greater creativity with the main latent’s temporal consistency.The paper frames this pairing as a trade-off between diversity and quality.Source: §4 · p. 5

3.4. Temporal Consistency Constraints

  • Depth maps guide the frozen UNet’s down-sampling during DDIM inversion and denoising, steering edits toward the original video’s motion trails and scene transformations.The paper presents this guidance as a way to improve edited-video stability and consistency.Source: §3.4 · p. 5
  • Frame-Align Attention replaces self-attention keys and values with first-frame features, emphasizing both previous and current frames for temporal encoding.The paper proposes it because conventional self-attention encodes each frame separately.Source: §3.4 · p. 5, Figure 3 · p. 5
  • Table 1 compares EVE with FateZero and ablates the two temporal-consistency constraints across four editing missions and their averages, using one A40 GPU under the same setting.The missions are object replacement, object adding, style transfer, and background changing.Source: Table 1 · p. 6

4. Experiments

The experiments introduce ZVE-50, describe the evaluation setup, and compare EVE with existing methods. EVE improves over FateZero on both reported consistency metrics and editing time, while ablations examine its temporal-consistency components.

4.1. Dataset Construction

  • ZVE-50 contains 50 videos from DAVIS-2017 and stock-video-footage, with four driven-text types and manually checked video, caption, and prompt content.BLIP2 captions are generated from each video, with the longest candidate selected as ground truth.Source: §4.1 · p. 6

4.2. Experimental Settings

  • The evaluation freezes a pretrained Latent Diffusion Model, samples eight 512×512 frames per video, and measures Temporal Consistency and Prompt Consistency.Editing uses 50 DDIM inversion and denoising steps; Prompt Consistency averages Text-Video CLIP scores across frames.Source: §4.2 · p. 6

4.3. Performance and Efficiency Comparisons

  • 83s vs. 247s: EVE takes about one-third of FateZero’s editing time; Table 2 compares EVE, FateZero, and Tune-A-Video on one A40 GPU.Tune-A-Video takes about 30 minutes to generate an edited video.Source: §4.3 · p. 7, Table 2 · p. 7
  • On ZVE-50, EVE beats FateZero across all four tasks, averaging +0.63% Temporal Consistency and +1.28% Prompt Consistency.The authors report these gains for the A1-versus-A2 comparison.Source: §4.3 · p. 7

4.4. Ablation Study

  • Ablations report that depth-map guidance prevents temporal distortion, while Frame-Align Attention improves temporal encoding and outperforms Sparse-Causal Attention on both consistency metrics across all four tasks.The reported comparison is between B4 with SCA and B5 with FAA.Source: §4.4 · p. 7

4.5. Visualization Results and Applications

  • EVE is demonstrated on four editing applications: object replacement, object adding, style transfer, and background changing.The examples include changing a man to a woman, adding glasses, applying Van Gogh style, and placing the scene under stars.Source: §4.5 · p. 7

5. Conclusion

The conclusion presents EVE as a zero-shot video editor that balances editing performance and efficiency through depth maps and temporal-consistency constraints. It also introduces ZVE-50 as a public benchmark for future comparisons.

5. Conclusion

  • EVE uses depth maps and two temporal-consistency constraints to guide zero-shot video editing.The authors frame these constraints as a response to videos’ need for temporal consistency.Source: §5 · p. 7
  • The authors characterize EVE’s outcome as a satisfactory trade-off between editing performance and efficiency.Source: §5 · p. 7
  • ZVE-50 is presented as a public video-editing dataset intended to support fair comparisons in future research.Source: §5 · p. 7

6. Future Work

The authors identify narrowing the performance gap between zero-shot and tuning-based video editing as future work. They suggest temporal-stability and knowledge-distillation directions.

6. Future Work

  • The stated future goal is to narrow the quality gap between zero-shot and tuning-based video editing methods.Source: §6 · p. 7
  • Proposed directions include adding triplet attention to UNet attention blocks and using inversion attention maps as pseudo-labels for knowledge distillation during denoising.Source: §6 · p. 7

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