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InstructVid2Vid: Controllable Video Editing with Natural Language Instructions

Bosheng Qin, Juncheng Li, Siliang Tang, Tat-Seng Chua, Yueting Zhuang

arXiv:2305.12328v2cs.CVcs.AIcs.MM

TL;DR

Video editing methods described here require fine-tuning for each input video, hindering editing efficiency. InstructVid2Vid uses synthetic video-instruction training triplets and a diffusion-based, instruction-guided editing pipeline; experiments report diverse edits with temporal coherence.

  • Problem

    Prior video-editing approaches require fine-tuning for each video, a process that substantially impedes editing efficiency.

    Source: §I · p. 1

  • Method

    InstructVid2Vid combines synthesized video-instruction training triplets with a diffusion-based editing model that uses instructions and input videos, without per-example fine-tuning or inversion.

    Source: §I · pp. 1–2

  • Results

    InstructVid2Vid performs attribute modification, background changes, and style transfer while preserving temporal information and original motion.

    Source: A. Visualization Results · p. 5

  • Takeaways & Limitations

    The method demonstrates instruction-guided video editing across attribute, background, and style changes with temporal coherence.

    Source: §VI · p. 6

Abstract

from arXiv · show

We introduce InstructVid2Vid, an end-to-end diffusion-based methodology for video editing guided by human language instructions. Our approach empowers video manipulation guided by natural language directives, eliminating the need for per-example fine-tuning or inversion. The proposed InstructVid2Vid model modifies a pretrained image generation model, Stable Diffusion, to generate a time-dependent sequence of video frames. By harnessing the collective intelligence of disparate models, we engineer a training dataset rich in video-instruction triplets, which is a more cost-efficient alternative to collecting data in real-world scenarios. To enhance the coherence between successive frames within the generated videos, we propose the Inter-Frames Consistency Loss and incorporate it during the training process. With multimodal classifier-free guidance during the inference stage, the generated videos is able to resonate with both the input video and the accompanying instructions. Experimental results demonstrate that InstructVid2Vid is capable of generating high-quality, temporally coherent videos and performing diverse edits, including attribute editing, background changes, and style transfer. These results underscore the versatility and effectiveness of our proposed method.

I. INTRODUCTION

InstructVid2Vid edits videos from natural-language instructions without per-video fine-tuning or inversion. It combines synthetic training triplets and an inter-frame loss to support diverse, temporally coherent edits.

I. INTRODUCTION

  • A composed pipeline using ChatGPT, a video-caption model, and Tune-a-Video synthesizes video-instruction triplets more cost-efficiently than real-world collection.The dataset is intended to train InstructVid2Vid.Source: §I · pp. 1–2
  • The end-to-end pipeline edits input videos from human-language instructions without example-specific fine-tuning or inversion.It does not require extra images or videos, or comprehensive descriptions of the input and output videos.Source: §I · pp. 1–2
  • The Inter-Frames Consistency Loss is incorporated during training to enhance temporal consistency between adjacent frames in edited videos.The loss guides generation of smooth edits consistent with the instructions and input videos.Source: §I · pp. 1–2
  • InstructVid2Vid performs attribute editing, background alteration, and style transfer while producing high-quality, temporally coherent edited videos.The introduction reports these capabilities as experimental results.Source: §I · p. 2

II. RELATED WORK

Prior text-driven video-editing approaches include edit-layer generation and methods based on fine-tuning or inversion. InstructVid2Vid instead follows human-language instructions without per-video tuning or additional media inputs.

A. Text Driven Video Editing

  • Dreamix [2], Video-P2P [3], and Turn-A-Video [4] use fine-tuning approaches for video editing or generation, while Pix2Video [11] uses DDIM inversion.Dreamix uses mixed video-image fine-tuning; Video-P2P uses cross-attention control, and Turn-A-Video uses one-shot fine-tuning.Source: A. Text Driven Video Editing · pp. 2–3
  • Text2Live generates an edit layer that is composited over an input image or video to add visual effects.Source: A. Text Driven Video Editing · p. 2
  • InstructVid2Vid follows human-language instructions without example-specific fine-tuning or inversion, masks, or extra images or videos.Source: A. Text Driven Video Editing · p. 3

III. METHOD

The method builds supervised training triplets by generating captions, instructions, and edited videos, then trains InstructVid2Vid to edit videos from text-video inputs.

III. METHOD

  • InstructVid2Vid is a supervised model trained on a dataset, with the method organized around dataset generation and the model framework.Source: §III · p. 3

1) Generate Video Captions, Instructions, and Edited Captions :

  • A video-caption model describes each input video, then ChatGPT generates instructions and edited captions from those descriptions.The text-domain prompting process can generate instructions and captions using a few handwritten examples.Source: 1) Generate Video Captions,… · p. 3

2) Generate Paired Videos :

  • Tune-a-Video is fine-tuned on each training clip, then uses the edited captions to generate temporally consistent videos reflecting the instructions.The resulting input videos, instructions, and edited videos form the training triplets.Source: 2) Generate Paired Videos · p. 3

B. InstructVid2Vid Model

  • The model section introduces Inter-Frames Consistency Loss and multimodal classifier-free guidance for text-video inputs.Source: B. InstructVid2Vid Model · p. 3

1) Model Architecture :

InstructVid2Vid encodes an input video and instruction, denoises them with a video-adapted network, and decodes edited frames. Training adds an inter-frame consistency loss to guide smoother edits while retaining temporal consistency.

1) Model Architecture :

  • The model encodes each video frame spatially and the instruction with CLIP, then denoises the encoded inputs and decodes synthesized edited frames.Its components include a VAE encoder, CLIP text encoder, denoising network, and VAE decoder.Source: 1) Model Architecture · p. 3
  • The denoising network adapts Stable Diffusion’s 2D U-Net with spatial and temporal convolutions and attention to process video.Spatial parameters are inherited from pretrained text-to-video Stable Diffusion, while temporal parameters are randomly initialized.Source: 1) Model Architecture · p. 3

2) Training with Inter-Frames Consistency Loss :

  • During training, only the denoising network is optimized: it is trained to correctly predict the added noise while preserving temporal consistency between adjacent frames.The VAE encoder, CLIP text encoder, and VAE decoder are fixed, and λ = 10^-3 by default.Source: 2) Training with Inter-Frames… · pp. 3–4
  • Inter-Frames Consistency Loss penalizes divergence between anticipated characteristics of successive frames and is incorporated into training.The passage defines v_i as the encoded features of frame i and f as the number of frames.Source: 2) Training with Inter-Frames… · pp. 3–4
  • Figure 2 depicts attribute modification, background changes, and style transfer from an input video and text prompt.Table II evaluates the impact of the consistency loss on video smoothness and quality; its caption does not report the outcome.Source: Figure 2 · p. 4, Table II · p. 4

3) Inference with Multimodal Classifier-Free Guidance :

At inference, InstructVid2Vid conditions on both the input video and text instruction. Its multimodal classifier-free guidance combines video and text guidance to steer the generated video toward both inputs.

  • Inference uses a dual-condition framework with a video condition and a textual condition to synthesize edited videos.The method describes this as a shift from classifier-free guidance with one input to dynamically allocating probability mass across both inputs.Source: 3) Inference with Multimodal… · p. 4
  • Training randomly drops the video condition for 5% of data, the text condition for another 5%, and both conditions for the remaining 5%, supporting conditional or unconditional denoising.Source: 3) Inference with Multimodal… · pp. 4–5
  • At inference, the estimated noise combines unconditional, video-conditioned, and video-and-text-conditioned estimates using separate video and text guidance scales.The passage states that this shifts probability mass toward higher P(cV | z) and P(cT | cV, z).Source: 3) Inference with Multimodal… · p. 5

IV. EXPERIMENTS

The experiments section examines attribute modification, background alteration, and style transfer while assessing temporal consistency across video types.

  • The experiments examine attribute modification, background alteration, and style transfer across videos.Source: §IV · p. 5
  • The section considers whether these editing tasks maintain temporal consistency across different types of videos.Source: §IV · p. 5
  • The authors describe planned quantitative and qualitative analyses to illustrate InstructVid2Vid’s performance.Source: §IV · p. 5

V. EVALUATION METRICS

The evaluation uses frame differencing, optical flow, and block matching to assess consistency between adjacent frames, alongside NR-PSNR and FID for video-frame quality.

V. EVALUATION METRICS

  • Frame differencing, optical flow, and block matching assess consistency between adjacent frames using pixel differences, motion fields, and matching blocks.Higher frame-differencing values indicate less smoothness; lower block-match scores or larger motion vectors may indicate poor smoothness.Source: §V · p. 5
  • NR-PSNR estimates video-frame quality without requiring a reference video, with higher values typically indicating better quality.The estimate uses a 3x3 Laplacian operator for edge detection and a logarithm of the peak-intensity-to-noise estimate ratio.Source: §V · p. 5
  • FID compares high-level feature distributions of generated and real video frames, with lower scores indicating greater similarity.The features are extracted using an Inception network.Source: §V · p. 5

A. Visualization Results

The visualizations show InstructVid2Vid making attribute, background, and style edits while retaining motion or temporal information and, where specified, the main subjects or semantics.

A. Visualization Results

  • Attribute edits change object colors, appearances, or vehicle types while maintaining the original environment and motion.Examples include changing car colors and soldiers’ attire.Source: A. Visualization Results · p. 6
  • Background edits change scenes, lighting, or weather while preserving the main subjects and temporal information.Examples include cherry-blossom and desert scenes, a snowy night, and a thunderstorm.Source: A. Visualization Results · p. 6
  • Style transfer changes visual style while preserving motion and semantics.Examples include gingerbread-house, oil-paste, and textile-art styles.Source: A. Visualization Results · p. 6

B. Quantitative and Qualitative Analysis

InstructVid2Vid outperforms Vid2Vid-Zero on the reported consistency and video-quality indicators; qualitative comparisons also report better source matching and frame stability, while the loss ablation shows improved stability and quality.

B. Quantitative and Qualitative Analysis

  • Qualitatively, InstructVid2Vid’s edited videos more closely match the source input, with reduced non-conforming deformations and greater similarity across adjacent-frame shapes and appearances.The comparison is with Vid2Vid-Zero.Source: B. Quantitative and Qualitative Analysis · p. 6
  • With Inter-Frames Consistency Loss, generated videos show improved stability and fewer glitches than the ablation without the loss.The ablation comparison also reports higher frame consistency and superior video quality with the loss.Source: B. Quantitative and Qualitative Analysis · p. 6
  • InstructVid2Vid outperforms Vid2Vid-Zero with lower frame-differencing and optical-flow scores, higher block-matching scores, higher NR-PSNR, and lower FID.The five indicators cover adjacent-frame consistency and video quality.Source: B. Quantitative and Qualitative Analysis · p. 6

VI. CONCLUSION

The conclusion presents InstructVid2Vid as an end-to-end diffusion-based video-editing method guided by human language instructions, avoiding example-specific fine-tuning or inversion. It reports attribute, background, and style edits while preserving object integrity and temporal coherence.

VI. CONCLUSION

  • InstructVid2Vid is an end-to-end diffusion-based video-editing method guided by human language instructions.The conclusion contrasts it with approaches requiring example-specific fine-tuning or inversion.Source: §VI · p. 6
  • The method avoids example-specific fine-tuning or inversion for video editing.Source: §VI · p. 6
  • The reported edits include attribute modification, background changes, and style transfer while preserving object integrity and temporal coherence.Source: §VI · p. 6
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