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Edit Temporal-Consistent Videos with Image Diffusion Model

Yuanzhi Wang, Yong Li, Xiaoya Zhang, Xin Liu, Anbo Dai, Antoni B. Chan, Zhen Cui

arXiv:2308.09091v2cs.CV

TL;DR

Prior text-guided video editors do not faithfully model temporal characteristics, leaving temporal consistency a challenge. TCVE combines a pretrained spatial T2I Unet, a dedicated temporal Unet, and a spatial-temporal modeling unit; it reports the best performance under three evaluation metrics. The authors also identify a limitation when style, objects, and backgrounds are manipulated simultaneously.

  • Problem

    Prior video-editing methods inject temporal attention into spatial-only T2I Unets, so temporal modeling may be unreliable because its inputs are only spatial-aware.

    Source: §I · p. 2

  • Method

    TCVE combines a pretrained T2I 2D Unet with a dedicated temporal Unet and a spatial-temporal modeling unit that bridges their features.

    Source: §I · p. 2

  • Results

    TCVE achieves the best video-editing performance under three evaluation metrics, with considerable improvements in Frame Consistency and Textual Alignment.

    Source: 1) Quantitative results · p. 6

  • Takeaways & Limitations

    Quantitative and visualization results indicate that TCVE mitigates flickering artifacts and temporal inconsistency in edited videos.

    Source: §I · p. 2

    TCVE may produce unsatisfactory results when style, objects, and backgrounds are manipulated simultaneously.

    Source: §VI · p. 9

Abstract

from arXiv · show

Large-scale text-to-image (T2I) diffusion models have been extended for text-guided video editing, yielding impressive zero-shot video editing performance. Nonetheless, the generated videos usually show spatial irregularities and temporal inconsistencies as the temporal characteristics of videos have not been faithfully modeled. In this paper, we propose an elegant yet effective Temporal-Consistent Video Editing (TCVE) method to mitigate the temporal inconsistency challenge for robust text-guided video editing. In addition to the utilization of a pretrained T2I 2D Unet for spatial content manipulation, we establish a dedicated temporal Unet architecture to faithfully capture the temporal coherence of the input video sequences. Furthermore, to establish coherence and interrelation between the spatial-focused and temporal-focused components, a cohesive spatial-temporal modeling unit is formulated. This unit effectively interconnects the temporal Unet with the pretrained 2D Unet, thereby enhancing the temporal consistency of the generated videos while preserving the capacity for video content manipulation. Quantitative experimental results and visualization results demonstrate that TCVE achieves state-of-the-art performance in both video temporal consistency and video editing capability, surpassing existing benchmarks in the field.

I. INTRODUCTION

Text-guided video editing can leverage pretrained T2I models, but prior approaches still show temporal inconsistency. TCVE combines a dedicated temporal Unet with a spatial-temporal modeling unit, and experiments report reduced flicker and inconsistency.

I. INTRODUCTION

  • Pretrained T2I models are an attractive basis for video editing given the availability of open-source models and the resource demands of training or refining T2V models.Source: §I · p. 1
  • Prior T2I-based video editors still show flicker because temporal modeling is incomplete and temporal-attention inputs are merely spatial-aware.The introduction identifies this limitation in earlier approaches.Source: §I · pp. 1–2
  • TCVE pairs a dedicated temporal Unet with a spatial-temporal modeling unit that connects it to the pretrained T2I 2D Unet.The unit captures spatial and temporal information while preserving video editing capability.Source: §I · p. 2
  • Extensive experiments report superior or comparable results and mitigation of flickering artifacts and temporal inconsistency.The evidence includes quantitative and visualization results.Source: §I · p. 2

II. RELATED WORKS

The section traces text-to-image generation from early conditional models to diffusion approaches. It highlights latent diffusion as an efficient and widely used paradigm for prompt-guided image generation.

A. Text-to-image Generation

  • Text-to-image generation aims to create photorealistic images that match text prompts using generative models conditioned on text embeddings.Source: A. Text-to-image Generation · p. 2
  • The field progressed from AlignDRAW to text-conditional GANs and autoregressive systems such as DALL-E and Parti.Source: A. Text-to-image Generation · p. 2
  • Diffusion models have achieved strong T2I results, and latent diffusion improves training efficiency by operating in an autoencoder's latent space.The paper uses LDM as its backbone and notes related extensions including ControlNet.Source: A. Text-to-image Generation · p. 2

B. Text-to-video Generation

The related work describes constraints on text-to-video generation and video-editing approaches built on pretrained T2I models. It also summarizes latent diffusion as an efficient backbone for the proposed method.

B. Text-to-video Generation

  • Text-to-video generation lags partly because large-scale text-video datasets are scarce and training is much harder than for T2I diffusion models.Prior systems include video diffusion, Imagen Video, Video LDM, and T2I-based motion modules.Source: B. Text-to-video Generation · p. 2

C. Text-guided Video Editing

  • Prior video editors use layered atlases or resource-intensive video diffusion, while Tune-A-Video and FateZero still exhibit flicker from incomplete temporal modeling.TCVE instead uses an independent temporal branch alongside a pretrained 2D Unet.Source: C. Text-guided Video Editing · p. 3, Figure 2 · p. 3

A. Latent Diffusion Models

  • Latent diffusion encodes images into a lower-resolution latent space, where a denoising Unet learns to recover clean latents from noise conditioned on text.The passage defines the prompt embedding, diffused sample, and sampling distributions used by the objective.Source: A. Latent Diffusion Models · p. 3

B. DDIM Sampler and Inversion

DDIM sampling iteratively turns Gaussian noise into a clean latent, while DDIM inversion projects a clean latent into noise. The inverted latent can then be denoised under an edited prompt and decoded as an edited image.

B. DDIM Sampler and Inversion

  • DDIM sampling converts Gaussian noise zT to a clean latent z0 over timesteps T→1.The sampler uses an iteration rule; αt is the noise-scheduling parameter.Source: B. DDIM Sampler and Inversion · p. 3
  • DDIM inversion projects a clean latent z0 into a noisy latent ẑT over the reversed timestep sequence.The inverted latent can be denoised back to a clean latent state.Source: B. DDIM Sampler and Inversion · pp. 3–4
  • With edited-prompt conditioning, the denoised latent is passed to decoder D to produce an edited image.The cited image-editing procedure uses a large classifier-free guidance scale factor (≫1).Source: B. DDIM Sampler and Inversion · p. 4

IV. METHOD

The method edits a source video under an edited prompt while aiming to preserve its motion. It combines a pretrained T2I 2D Unet for spatial manipulation with a temporal Unet for temporal coherence.

IV. METHOD

  • Text-guided video editing generates Vedit from source video V conditioned on edited prompt pedit.The source prompt psour describes V, while pedit specifies the target edit.Source: A. Problem Formulation · p. 4
  • TCVE combines a pretrained 2D T2I Unet for spatial content manipulation with a dedicated temporal Unet for video coherence.The temporal network uses temporal convolutional layers to model video information.Source: A. Problem Formulation · p. 4
  • The source video is encoded and DDIM-inverted, then denoised under pedit after flattening into spatial- and temporal-dominated tensors for the respective Unets.The generated frames are decoded to form the edited video.Source: A. Problem Formulation · p. 4
  • Fspa and Ftem denote the flattening operations, while θsps and θtem denote the 2D Unet and temporal-model parameters.These symbols specify the spatial and temporal branches of the generation process.Source: A. Problem Formulation · p. 4

B. Network Architecture

The architecture pairs a spatial T2I 2D Unet with a temporal Unet and connects their features through a Spatial-Temporal modeling Unit. The modules are designed to preserve editing while improving temporal consistency.

B. Network Architecture

  • The spatial-only T2I 2D Unet lacks dynamic sequence modeling, so video generation without an expert sequence model can produce flickering artifacts.Its spatial self-attention captures pixel-location dependencies, while cross-attention relates image and prompt features.Source: B. Network Architecture · p. 4
  • The Temporal Unet reshapes each video into sequences along the frame axis and processes them with temporal convolutional residual blocks in a downsampling–upsampling architecture.This temporal-aware input and output is intended to model temporal information consistently.Source: B. Network Architecture · p. 4
  • The STU connects spatial- and temporal-aware features using temporal attention, weighted fusion, and a 3D convolutional block.Temporal attention captures dependencies between features at the same spatial location across time; λ = 0.1 balances the fusion.Source: B. Network Architecture · pp. 4–5
  • In the style-transfer visualization, TCVE produces temporally consistent videos while transferring style; the cited baselines show frame inconsistency or foreground texture disruption.Tune-A-Video and FateZero may show frame inconsistency, while StableVideo tends to disrupt foreground texture.Source: Figure 3 · p. 5

C. Training and Inference

TCVE trains its temporal Unet and STUs on source videos and prompts while keeping the pretrained T2I 2D Unet frozen. At inference, the method edits target videos, and experiments report accurate delivery of source motion and structure.

C. Training and Inference

  • Training uses the source video and source prompt to train the temporal Unet and STUs with the original LDM objective, while freezing the pretrained T2I 2D Unet.Training does not need to be repeated for each edited prompt, making the method a zero-shot video-editing paradigm.Source: C. Training and Inference · p. 5
  • At inference, TCVE edits the target video using the defined procedure, and experiments report accurate delivery of source motion and structure to edited videos.The inference procedure is referred to as Eq. 4.Source: C. Training and Inference · p. 5

V. EXPERIMENTS

Experiments use pretrained Stable Diffusion and compare TCVE with three video-editing baselines across four editing tasks, using three video-quality metrics.

A. Implementation Details

  • TCVE is based on pretrained Stable Diffusion v1.4, and training uses 100 iterations per video with Adam at a 3 × 10−5 learning rate.Inference uses DDIM inversion and sampling for 50 steps with classifier-free guidance scale 12.5.Source: A. Implementation Details · pp. 5–6
  • The reported experiments use video samples in, with each video receiving four edited prompts spanning style transfer, object editing, background change, and multiple-object editing.Examples replace two gray sharks with two quadrotor drones and change a shopping and entertainment center to a Martian landscape.Source: A. Implementation Details · p. 5
  • Evaluation measures frame consistency, textual alignment, and PickScore using CLIP-based comparisons of output frames.Frame consistency averages pairwise frame-embedding cosine similarity; the other metrics compare output frames with the edited prompt.Source: A. Implementation Details · p. 6

B. Baseline Comparisons

  • TCVE is compared with Tune-A-Video, FateZero, and StableVideo, representing pretrained-T2I video editing and atlas-based editing approaches.FateZero is described as adding a fusing-attention mechanism to Tune-A-Video.Source: B. Baseline Comparisons · p. 6

1) Quantitative results:

TCVE is reported to lead the evaluated baselines on all three metrics, while qualitative comparisons show editing across style, objects, backgrounds, and multiple objects.

1) Quantitative results:

  • TCVE achieves the best video-editing performance under all three evaluation metrics, with considerable improvements in Frame Consistency and Textual Alignment.The quantitative comparison is reported in Table I; the supplied passages do not give its metric values.Source: Table I · p. 6, 1) Quantitative results · p. 6

2) Qualitative results:

  • In style transfer, TCVE produces temporally smooth videos while editing style, whereas baselines show disappearing objects, distortions, or inconsistent appearance.Examples include a man disappearing, a car distortion, and inconsistent horse color.Source: 2) Qualitative results · pp. 6–7
  • For object editing, TCVE changes objects to match prompts while preserving other video attributes; baselines show flicker, incorrect object counts, or limited editability.StableVideo’s atlas-based approach is described as unable to edit and deform foreground objects.Source: 2) Qualitative results · p. 7
  • For background changes, TCVE changes the setting to match the prompt while preserving surfers’ poses; the three baselines fail on one illustrated setting change.Source: 2) Qualitative results · p. 7, Figure 5 · p. 7
  • In multiple-object editing, TCVE edits backgrounds and objects with enhanced temporal coherence, while Tune-A-Video and FateZero show foreground inconsistencies.All methods except StableVideo change the background in the two illustrated samples.Source: 2) Qualitative results · p. 7

C. Ablation Studies

Ablations indicate that the Temporal Unet supports frame consistency and the STU helps retain both target-prompt alignment and temporal consistency.

1) Exploring effects of the key components in TCVE:

  • The Temporal Unet improves frame consistency, while the STU further aligns edited videos with the target prompt and enhances temporal consistency.Table II reports ablations of TCVE’s key components.Source: 1) Exploring effects of the key… · p. 7, Table II · p. 8
  • Without the Temporal Unet, the escalator deforms differently across frames; without the STU, frames remain smooth but miss the targeted edit.TCVE preserves the escalator’s structure and combines smooth video with the intended edit.Source: 1) Exploring effects of the key… · p. 8

2) Exploring effects of the key components in STU:

STU ablations associate temporal attention with frame consistency and 3D convolution with textual alignment; the conclusion also notes a boundary for simultaneous multi-aspect editing.

2) Exploring effects of the key components in STU:

  • Removing temporal attention degrades frame consistency, while removing 3D convolution reduces textual alignment, supporting their respective roles.Table III reports ablations of STU’s components.Source: 2) Exploring effects of the key… · pp. 8–9, Table III · p. 9
  • The ablation visual contrasts flickering and editing failure without TU or STU with TCVE’s joint consistency and editing capability.Without TU, the escalator deforms to varying degrees across frames; without STU, frames are smooth but the targeted edit fails.Source: Figure 7 · p. 8

VI. CONCLUSION

  • TCVE may produce unsatisfactory results when simultaneously manipulating style, objects, and backgrounds; the authors attribute this possibly to image-oriented CLIP text embeddings.A video-based CLIP model is proposed as a potential solution and left for future work.Source: §VI · p. 9

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