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CCEdit: Creative and Controllable Video Editing via Diffusion Models

Ruoyu Feng, Wenming Weng, Yanhui Wang, Yuhui Yuan, Jianmin Bao, Chong Luo, Zhibo Chen, Baining Guo

arXiv:2309.16496v3cs.CV

TL;DR

Generative video editing must balance diverse editing requests and precise control with temporal coherence. CCEdit uses a trident network to separate structure and appearance control, and its reported comparisons favor CCEdit while an ablation reports higher MOS for edited-key-frame propagation than for no appearance control.

  • Problem

    Generative video editing must support diverse editing requests and fine-grained control while preserving temporal coherence across frames.

    Source: §1 · p. 2, §2.2 · p. 3

  • Method

    CCEdit uses a trident network with ControlNet structure conditioning and an appearance branch for an edited key frame, integrated with a text-to-video main branch through temporal layers.

    Source: §3.2 · p. 4

  • Results

    CCEdit excels in editing accuracy in the reported comparison, and the edited-key-frame process scored MOS 3.87 versus 2.88 without appearance control.

    Source: §5.3 · p. 8, §5.4 · p. 8

  • Takeaways & Limitations

    The paper presents CCEdit and BalanceCC as tools for creative workflows and evaluation in generative video editing.

    Source: §1 · p. 2, §7 · p. 9

    Structural control may struggle with substantial structural alterations, such as changing a cute rabbit into a majestic tiger.

    Source: §6 · p. 8

Abstract

from arXiv · show

In this paper, we present CCEdit, a versatile generative video editing framework based on diffusion models. Our approach employs a novel trident network structure that separates structure and appearance control, ensuring precise and creative editing capabilities. Utilizing the foundational ControlNet architecture, we maintain the structural integrity of the video during editing. The incorporation of an additional appearance branch enables users to exert fine-grained control over the edited key frame. These two side branches seamlessly integrate into the main branch, which is constructed upon existing text-to-image (T2I) generation models, through learnable temporal layers. The versatility of our framework is demonstrated through a diverse range of choices in both structure representations and personalized T2I models, as well as the option to provide the edited key frame. To facilitate comprehensive evaluation, we introduce the BalanceCC benchmark dataset, comprising 100 videos and 4 target prompts for each video. Our extensive user studies compare CCEdit with eight state-of-the-art video editing methods. The outcomes demonstrate CCEdit's substantial superiority over all other methods.

1. Introduction

Diffusion models have advanced generative video editing, but editors still face challenges in meeting diverse requests and exercising precise control. CCEdit separates structure and appearance control, offers multiple control options, and introduces BalanceCC for evaluation.

1. Introduction

  • Diffusion-based generative models have advanced image and video creation and opened opportunities for generative video editing.The paper situates CCEdit amid prior work on prompt-conditioned image and video generation.Source: §1 · p. 2
  • Generative video editing must accommodate diverse requests, provide fine-grained control, and harness creativity while aligning outputs with editors’ intentions.The paper names stylistic alterations, foreground replacements, and background modifications as examples of editing requirements.Source: §1 · p. 2
  • CCEdit decouples structure and appearance control in a trident network whose main text-to-video branch integrates two side branches through learnable temporal layers.The main branch uses a pretrained text-to-image diffusion model adapted to video with temporal modules; the temporal layers also help ensure consistency across generated frames.Source: §1 · p. 2
  • Users can choose line drawings, PiDi boundaries, or depth maps for structure control, and prompts, personalized T2I models, or an edited key frame for appearance control.The paper names DreamBooth and LoRA as examples of personalized models that can be integrated as plugins.Source: §1 · p. 2
  • CCEdit introduces BalanceCC, a benchmark of 100 diverse videos with four target prompts each and detailed scene descriptions and attributes.The descriptions cover video category, scene complexity, and motion; GPT-4V-assisted descriptions were refined by human annotators.Source: §1 · p. 2

2. Related Work

Diffusion models underpin prompt-conditioned image and video synthesis, while video editing must also preserve coherence across frames. Prior editing methods pursue temporal consistency through attention changes or per-video tuning; CCEdit instead proposes dataset-level fine-tuning focused on temporal layers.

2.1. Diffusion-based Image and Video Generation

  • Diffusion models support image synthesis through iterative denoising and prompt conditioning, and video generation uses spatial-temporal modules to maintain temporal consistency.Figure 2 depicts CCEdit’s structure and appearance information as independently modulated before integration into the main branch.Source: §2.1 · p. 2, Figure 2 · p. 3

2.2. Video Editing with Diffusion Models

  • Video editing must manipulate appearance while preserving temporal coherence, since lapses can produce flickering and degradation.Source: §2.2 · p. 3
  • Prior methods seek temporal consistency through cross-frame attention or per-video fine-tuning, which can be time-consuming and may yield suboptimal coherence if temporal modules are inadequately tuned.Other work trains temporal layers on extensive text-video paired datasets for generation and editing.Source: §2.2 · p. 3
  • CCEdit instead uses dataset-level fine-tuning to train temporal layers, avoiding per-video tuning at inference rather than modifying T2I models with a simple fix or training a full T2V model.Source: §2.2 · p. 3

3. Approach

CCEdit builds a diffusion video editor by combining a T2I-derived temporal main branch with separate structure and key-frame appearance controls. For long clips, extension and interpolation reuse edited frames as references.

3.1. Preliminary

  • Diffusion models learn to denoise progressively corrupted inputs; latent diffusion performs this optimization in learned latent space, linked to pixels by encoder E and decoder D.The latent representation is defined as z = E(x), with x approximately reconstructed by D(E(x)).Source: §3.1 · pp. 3–4

3.2. The CCEdit Framework

  • The main branch converts pretrained Stable Diffusion into a video model with temporal layers, while training updates temporal layers and appearance-branch weights but freezes spatial and structure-branch weights.The model uses the pretrained T2I spatial weights to retain compatibility and visual generation capability.Source: §3.2 · pp. 4–5
  • The ControlNet structure branch injects per-frame structural representations into the main branch, with its weights frozen during training.Representations can include line drawings, PiDi boundaries, or depth maps.Source: §3.2 · p. 4
  • The appearance branch encodes an edited key frame into multi-scale features that enter the main branch and propagate to all frames through temporal modules.The branch is initialized from a pretrained T2I encoder with text cross-attention layers removed.Source: §3.2 · pp. 4–5
  • Anchor prior adds a small amount of the edited key frame's latent to each frame's start noise to stabilize results when preprocessing yields inconsistent structure.The authors report that α = 0.03 works well in most cases.Source: §3.2 · p. 5

3.3. Editing for Long Videos

  • For long videos, extension processes successive key frames and interpolation edits the frames between adjacent edited references.The procedure repeats until all frames are edited.Source: §3.3 · p. 5

4. BalanceCC Benchmark

BalanceCC addresses the lack of a standardized benchmark by focusing evaluation on controllability and creativity. It combines 100 varied videos, target prompts, and categorized scene and motion attributes to support evaluation and targeted method improvement.

4.1. Overview

  • BalanceCC provides 100 videos with varied attributes as a platform for evaluating video editing methods on controllability and creativity.The authors identify the lack of a standardized benchmark as a potential hindrance to technical progress.Source: §4.1 · p. 5

4.2. Benchmark Establishment

  • The benchmark uses 100 open-license videos, GPT-4V-assisted scene descriptions, and human annotations of camera movement, object movement, and category.Videos are 2–20 seconds long at about 30 fps; target prompts cover style, object, background, and compound edits, with a Fantasy Level for imaginative degree.Source: §4.2 · pp. 5–6

4.3. Statistics

  • Figure 3 summarizes the dataset statistics: stationary and slow camera motion account for 54% and 38%, while fantasy levels are relatively balanced with a slight moderate-level tilt.The benchmark has slightly more human-category videos, a deliberate choice reflecting practical relevance and the complexity of human and facial structures.Source: §4.3 · p. 6, Figure 3 · p. 6
  • The authors intend the benchmark's categorization to help users understand method strengths and weaknesses and support targeted improvements.They describe this as a way to foster faster development in the field.Source: §4.3 · p. 6

5. Experiments

CCEdit is evaluated through controllable editing applications, comparison with eight video-editing methods, and ablations of its appearance control and anchor prior. The reported results cover user preferences, long-video consistency, and the effects of these controls.

5.1. Implementation Details

  • Stable Diffusion-v1.5 supplies the main-branch base model, with ControlNet guiding structure and training across several structural representations.Training combines WebVid-10M and a private dataset; depth maps are the default structural control.Source: §5.1 · p. 6

5.2. Applications

  • CCEdit supports style transfer with varied structural guidance and personalized T2I models, plus foreground, background, or whole-image edits guided by a customized center frame.The edited center frame is used as a reference to propagate the changes coherently through the video.Source: Figure 5 · p. 6, §5.2 · p. 7, Figure 6 · p. 7
  • CCEdit’s hierarchical key-frame editing, iterative extension, and temporal interpolation support editing videos with hundreds of frames at 24 fps.Figure 7 reports good consistency across more than 240 frames.Source: §5.2 · p. 7, Figure 7 · p. 7

5.3. State-of-the-Art Comparisons

  • 3.87/5 overall MOS for CCEdit versus 3.58 for TokenFlow; CCEdit wins 52.9%, loses 32.4%, and ties 14.7% in head-to-head comparisons.The user study also reports that CCEdit excels in editing accuracy and aesthetics and is slightly inferior to TokenFlow in temporal smoothness.Source: Table 1 · p. 8, §5.3 · p. 8

5.4. Ablation Study

  • 3.87 versus 2.88 MOS: editing a key frame and propagating it to surrounding frames outperforms appearance control without that branch.The customized frame can help in challenging cases where prompt-only “cyberpunk” editing is difficult for the pretrained T2I model and temporal modules.Source: §5.4 · p. 8, Figure 9 · p. 8
  • The anchor prior mitigates regional flickering, which may appear when the prior is absent.The red boxes in Figure 10 indicate localized flickering.Source: §5.4 · p. 8, Figure 10 · p. 8

6. Limitation and Future Works

Explicitly preserving source-video structure can make substantial structural changes challenging. The authors identify such alterations as a future-work objective.

6. Limitation and Future Works

  • Because the structure branch sustains the source video’s extracted representation, changes such as turning a cute rabbit into a majestic tiger may be challenging.Addressing substantial structural alterations is identified as future work.Source: §6 · p. 8

7. Conclusion

The conclusion presents CCEdit’s trident architecture and BalanceCC as contributions to generative video editing. The detailed architecture describes how its appearance and structure branches feed into the main branch.

7. Conclusion

  • The conclusion presents a unified trident network for controllable video editing and introduces BalanceCC as a benchmark resource for research and creative workflows.The paper describes the framework as broadening creative possibilities.Source: §7 · p. 9

A. Details of the Trident Network

  • CCEdit’s appearance branch encodes an edited key frame, while its ControlNet-based structure branch encodes source-video structure and injects features into the main branch.Temporal modules propagate key-frame appearance across frames while inherited structure supports structural fidelity.Source: A. Details of the Trident Network · p. 12
  • The appearance branch uses the VAE encoder to map pixel-wise appearance into the main branch’s latent representation, allowing its encoder weights to initialize the branch.The paper says this initialization accelerates and stabilizes convergence.Source: A. Details of the Trident Network · p. 12

B. BalanceCC Benchmark

BalanceCC is a video-editing benchmark with annotated videos and four edit prompts per video, assembled with model assistance and human refinement. The accompanying appendix describes its annotation process, personalized T2I models, visual examples, and comparison methods.

B. BalanceCC Benchmark

  • BalanceCC contains 100 open-license videos across four categories, with four edit types and target prompts per video and ratings for motion, scene complexity, and fantasy level.The edit types are Style Change, Object Change, Background Change, and Compound Change.Source: B. BalanceCC Benchmark · p. 12

B.1. Prompting Pipeline and Instructions

  • GPT-4V receives center frames and benchmark requirements to generate annotations; prompt revisions and reference examples address hallucinations, excessive detail, and forgetfulness.The authors manually verify and refine the initial outputs, adding camera- and object-motion details.Source: §B.1 · p. 12, §B.2 · p. 13

B.2. Human Refinement

  • The 1–3 scales describe camera motion, object motion, scene complexity, and fantasy, progressing from stationary or simple cases to rapid, complex, intricate, or creatively combined cases.Compound Change generally has Fantasy Level 3.Source: §B.2 · p. 13

B.3. Illustrative Examples

  • The paper points to four illustrative benchmark examples in Figure 15.Source: §B.3 · p. 13

C.1. Personalized T2I Models

  • The framework can integrate off-the-shelf personalized T2I models; the appendix describes DreamBooth concept indicators and LoRA residual updates to frozen base parameters.The paper also notes fine-tuning full models and blending model parameters as ways to create personalized bases.Source: §C.1 · pp. 13–14

C.2. More Visualizations

  • The appendix points to Figure 17 for additional visualized CCEdit results.Source: §C.2 · p. 14

C.3.1 Compared Methods

  • The comparison covers eight video-editing methods and uses 17-frame, 6-fps clips, depth maps, and Stable Diffusion v1.5 as the common base model.The methods include Tune-A-Video, vid2vid-zero, Text2Video-zero, FateZero, Pix2Video, ControlVideo, Rerender A Video, and TokenFlow.Source: §C.3.1 · pp. 14–15

C.3.2 Qualitative Results

CCEdit shows strong temporal consistency and editing accuracy in qualitative comparisons, while other methods exhibit distinct weaknesses in coherence, naturalness, or motion detail.

C.3.2 Qualitative Results

  • CCEdit notably sustains temporal consistency while achieving exceptional editing accuracy.The comparison covers two videos.Source: §C.3.2 · p. 15
  • Tune-A-Video aligns edits with prompts but can lose temporal consistency and produce overly contrasted images.The authors suggest overfitting to the source video and high default classifier-free guidance as possible reasons.Source: §C.3.2 · p. 15
  • Vid2vid-zero, Text2Video-Zero, and Pix2Video struggle with temporal coherence, while FateZero is more coherent but less accurate.Source: §C.3.2 · p. 15
  • ControlVideo has reasonable accuracy and coherence but lacks naturalness; Rerender A Video struggles with precise edits.The authors associate the latter limitation potentially with dependence on detailed structural controls.Source: §C.3.2 · p. 15
  • TokenFlow is stable in coherence and accuracy but can blur scenes with substantial object motion or rapid camera movement.The horse legs in Fig. 19 are given as an example.Source: §C.3.2 · p. 15

C.3.3 Quantitative Results

The evaluation combines automatic metrics and user ratings. CCEdit scores best on PickScore, although its objective temporal-consistency and text-alignment metrics do not particularly stand out.

C.3.3 Quantitative Results

  • PickScore, the CLIP-based scoring function, gives CCEdit the best performance and indicates superior alignment with human subjective perceptions.Source: §C.3.3 · p. 15, Table 3 · p. 15
  • CCEdit ranked second in temporal consistency and first in text alignment in the main-text user-study table, but its corresponding objective metrics did not particularly stand out.Source: §C.3.3 · p. 15
  • Thirty-three volunteers supplied 1,119 ratings on editing accuracy, aesthetics, temporal consistency, and overall impression.Each rating covered one video.Source: §C.3.3 · p. 15

C.3.4 Runtime Analysis

The runtime analysis reports that CCEdit is not lengthy relative to other techniques, while the control-scale and prompt studies show trade-offs in structure, appearance, and motion.

C.3.4 Runtime Analysis

  • CCEdit's total runtime is not lengthy compared with other techniques; key-frame editing dominates preprocessing, while sampling is relatively brief.The runtime comparison separates preprocessing, inference, and total duration.Source: §C.3.4 · pp. 15–16, Table 4 · p. 16

C.4. Study on Control Scales

  • A structure control scale of 0.2–0.4 offers a better appearance, structure, and motion trade-off; 0 loses needed structure, while 0.6–1.0 overconstrains other frames.The scale adjusts the intensity of structure features injected into the main branch.Source: §C.4 · p. 16, Figure 12 · p. 16
  • Appearance control has little effect at 0–0.2, can yield dull, dark visuals at 0.4–0.6, and decisively controls overall appearance at 0.8–1.0.Source: §C.4 · p. 16, Figure 13 · p. 17

C.5. Study on Text Prompt

  • Text prompts remain necessary: removing them distorts output, and a contradictory prompt leaves appearance control only in the center frame.The authors suggest prompts provide a coarse guide before the appearance branch adds detailed control.Source: §C.5 · pp. 16–17, Figure 13 · p. 17

D. Limitation and Future Works

The paper identifies structural deviation, heavy branch encoders, and latent-domain flickering as limitations, and outlines possible directions for addressing them.

D.1. Structural Deviation

  • Structural deviation can arise from semantic or behavioral changes; lower structure scale and coarser representations may help but do not fundamentally solve it.Changing target behavior is harder because T2I models lack action priors; a pretrained T2V model is proposed as a promising direction.Source: §D.1 · p. 17

D.2. Heavy Appearance and Structure Branch

  • The two heavy branch encoders add many parameters and could increase GPU memory use and editing time; lightweight networks are a planned direction.Source: §D.2 · p. 17

D.3. Flickering Problem.

  • Flickering appears at higher frame rates or after interpolation, especially in fine textures, and is attributed mainly to editing in a 2D-autoencoder latent domain.Adding temporal layers to the autoencoder is described as a promising way to address it.Source: §D.3 · p. 17
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