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VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models

Zhen Xing, Shuyuan Tu, Qi Dai, Zihao Zhang, Hui Zhang, Han Hu, Zuxuan Wu, Yu-Gang Jiang

arXiv:2311.18837v2cs.CVcs.AIcs.LGcs.MM

TL;DR

Unified video frameworks for understanding and editing remain scarce, and video editing often involves detailed prompts or time-consuming processes. VIDiff adapts pretrained image diffusion for multimodal video translation; the paper reports effectiveness across tasks, improved generalization with joint training, and consistency across long-video clips.

  • Problem

    Unified video frameworks spanning understanding and editing remain scarce, while editing methods often require detailed prompts, time-consuming processes, or support for few instruction scenarios.

    Source: §1 · p. 2

  • Method

    VIDiff adapts a pretrained text-to-image diffusion model for video translation using text and image instructions, with iterative inference for long videos.

    Source: §1 · p. 2, §3.4 · p. 4

  • Results

    Across evaluated tasks, VIDiff is reported effective, outperforms mainstream open-source editing techniques on enhancement benchmarks, and joint training and iterative inference improve generalization and long-video consistency.

    Source: §4.2 · p. 7, §4.3 · pp. 7–8, §5 · p. 8

  • Takeaways & Limitations

    The paper concludes that treating video understanding tasks as conditional translation offers a unified modeling interface for varied video tasks.

    Source: §5 · p. 8

Abstract

from arXiv · show

Diffusion models have achieved significant success in image and video generation. This motivates a growing interest in video editing tasks, where videos are edited according to provided text descriptions. However, most existing approaches only focus on video editing for short clips and rely on time-consuming tuning or inference. We are the first to propose Video Instruction Diffusion (VIDiff), a unified foundation model designed for a wide range of video tasks. These tasks encompass both understanding tasks (such as language-guided video object segmentation) and generative tasks (video editing and enhancement). Our model can edit and translate the desired results within seconds based on user instructions. Moreover, we design an iterative auto-regressive method to ensure consistency in editing and enhancing long videos. We provide convincing generative results for diverse input videos and written instructions, both qualitatively and quantitatively. More examples can be found at our website https://ChenHsing.github.io/VIDiff.

1. Introduction

Visual foundation models have largely focused on understanding, while unified video frameworks for understanding and editing remain scarce. VIDiff frames video tasks as instruction-guided translation and combines multimodal conditioning with staged training and iterative generation.

1. Introduction

  • Unified video frameworks for both understanding and editing remain scarce because video data and task variation are more complex than for images.Prior unified visual frameworks mainly address understanding, while generative video tasks have received less attention.Source: §1 · p. 2
  • Video editing methods often require detailed source and target descriptions, time-consuming tuning or inference, or support for only a few instruction-based scenarios.Producing precise output descriptions can also require domain knowledge, such as art or medicine.Source: §1 · p. 2
  • VIDiff translates a source video according to multimodal instructions, using images as straightforward guidance alongside text.The image instructions are presented as a way to guide edits without requiring expert knowledge for text prompts.Source: §1 · p. 2
  • VIDiff adapts a pretrained text-to-image model through multiple training stages and uses iterative generation for long-video translation.The paper presents the framework as unifying video understanding and editing tasks.Source: §1 · p. 2

2. Related Work

Related work covers visual foundation models, image translation, and video editing, while identifying unified video translation as a comparatively underexplored focus. VIDiff positions its approach as a generalist framework that also handles long-video editing.

Video Language Foundation Model

  • Video-language models commonly target understanding tasks, and video translation has received limited research attention [7] [59].Prior work unifies tasks such as classification, retrieval, temporal detection, question answering, segmentation, and tracking [27] [50].Source: §2 · p. 2

Text-guided Image Translation

  • Image translation methods include zero-shot diffusion, per-image optimization, and paired-data training, while video translation is described as more challenging than image translation [13].Per-image optimization can make editing time-consuming [25].Source: §2 · pp. 2–3

Text guided Video Editing

  • Video-editing methods use model fine-tuning or cross-attention control, while instruction-based approaches have emerged more recently [30] [40] [53] [63].VIDiff distinguishes its focus as a generalist framework for multiple video tasks that also applies to long videos.Source: §2 · p. 3

3. Method

VIDiff formulates video tasks as instruction-conditioned translation and adapts latent diffusion for this setting. Its method combines task-specific training data, a video-capable model, multimodal instructions, and iterative inference for long clips.

3.1. Preliminaries of LDM

  • Latent diffusion encodes inputs into a lower-dimensional latent space and performs diffusion and denoising there, with text conditions extracted by pretrained CLIP.The paper adapts this text-to-image model to video-to-video translation.Source: §3.1 · p. 3

3.2. Problem Definition

  • The method represents common video tasks as translating a source video and instruction into a corresponding target video.Examples include translating raw pixels into segmentation maps and grayscale frames into color video.Source: §3.2 · p. 3

3.3. Training Data Construction

  • Training triplets for recoloring and inpainting can be constructed from unlabeled videos, while enhancement data uses annotated input and ground-truth datasets.Instructions for these tasks are written as short task-specific phrases.Source: §3.3 · p. 3

3.4. Unified Instructional Model for Video Tasks

  • Training transfers a text-to-image model through text-to-video pretraining and final video-to-video fine-tuning.The stages are original text-to-image training, temporal-module training with video-text data, then tuning on collected video-translation datasets.Source: Figure 2 · p. 4, §3.4 · p. 4

3.3. Training Data Construction

  • Segmentation training uses established datasets and manually crafted instructions, while video-editing triplets are generated using GPT and existing editing models.At inference, editing requires the original video and instructions, rather than detailed source and target descriptions.Source: §3.3 · p. 4

3.4. Unified Instructional Model for Video Tasks

  • The video architecture inflates a text-to-image U-Net’s 2D convolutions to 3D and adds temporal attention for motion modeling.The temporal transformer output projection is zero-initialized to integrate the module into training without adverse effects.Source: §3.4 · p. 4
  • Text and augmented target-frame image embeddings are concatenated as joint instructions, with the CLIP encoders frozen and only the added MLP trained.The image instruction is intended to avoid the need for expert-level text instructions.Source: §3.4 · p. 4, Figure 3 · p. 4
  • Long-video training varies the number of source reference frames, and inference reuses the preceding clip’s last frames to condition the next clip.Overlapping iterative sampling is used to maintain translation consistency across clips.Source: §3.4 · p. 5, Figure 4 · p. 5

4. Experiments

The experiments evaluate VIDiff across video editing, recoloring, enhancement, and long-video translation, alongside ablations of joint training and multi-stage transfer. The reported results include improvements on perceptual metrics, video enhancement metrics, and temporal consistency across long-video clips.

4.1. Settings

  • Training uses triplets spanning tasks including dehazing, deblurring, recoloring, inpainting, object segmentation, and editing, with task selection at each training step.The model is initialized from Stable Diffusion v1.5, and training uses 16 frames at 256×256 resolution; inference supports arbitrary resolutions and aspect ratios.Source: §4.1 · pp. 5–6

4.2. Experimental Results

  • VIDiff reports significant perceptual improvements for recoloring and outperforms other methods across video-enhancement metrics, while maintaining comparable structural performance.The recoloring evaluation reports significant improvements on perceptual metrics and comparable performance on structural metrics; the enhancement evaluation reports outperformance across all metrics.Source: Table 3 · p. 7, §4.2 · p. 7, Table 4 · p. 8
  • Unlike many editing methods that require one-shot tuning and detailed source and target descriptions, VIDiff requires no additional tuning and supports multimodal instructions and long-video editing.The comparison covers editing-method attributes; the reported results also include quantitative measures, user-study results, and tuning and inference times.Source: Table 2 · p. 6, §4.2 · p. 7

4.3. Ablation Study

  • The proposed long-video translation approach maintains consistency between clips, unlike translations without its Long Video Translation constraints.The comparison is shown in Figure 6.Source: Figure 6 · p. 8, §4.3 · p. 8
  • The jointly trained model outperforms individually trained models across four task-specific test datasets, with the advantage also extending to video editing.The comparison is shown in Figure 7.Source: §4.3 · p. 7, Figure 7 · p. 8
  • Multi-stage transfer yields significantly better image quality than direct fine-tuning.The ablation is shown in Figure 7.Source: Figure 7 · p. 8, §4.3 · p. 8

5. Conclusion

VIDiff frames video understanding tasks as conditional video translation guided by human instructions. The authors report effectiveness across multiple tasks and identify large-language-model integration as a direction for future work.

5. Conclusion

  • VIDiff is a unified framework that treats video understanding tasks as conditional video translation guided by instructions.The conclusion describes this as aligning video tasks with human instructions.Source: §5 · p. 8
  • The authors report effectiveness across multiple tasks and say joint training enhances the model's generalization capabilities.Source: §5 · p. 8
  • Future work will explore integrating large language models for video question answering and contextual understanding.Source: §5 · p. 9

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