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

arXiv:2311.18837 · cs.CV, cs.AI, cs.LG, cs.MM · Submitted 2023-11-30 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom, and with me are Jane, Lu, senior AI researcher at Tsinghua, Meng, lead engineer at a mysterious AI startup and Lalam, the in-house Large Language Model.

Jane: Today's paper: "VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models".

Tom: Diffusion models have achieved significant success in image and video generation, motivating research into video editing tasks guided by natural language instructions.

Jane: First, who's behind it and why it matters.

Paper summary: Tom: Hey everyone! So we're diving into the latest paper from arXiv today, "VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models." It looks like this work aims to create a single model that can handle a whole bunch of video editing stuff using just natural language and some images as guidance.

Jane: That sounds really ambitious, Tom. The abstract mentions it's designed to tackle things like re-colorization, deblurring, inpainting, and even style transfer by taking both source videos and multimodal instructions.

Lu: It’s interesting how they are trying to build a generalist model for video translation tasks using diffusion models <ref:2311.18837#pg0>.

Meng: A generalist model sounds great on paper, but I'm curious about the practical application; how does it actually handle the complexity of different video domains?

Lalam: Well, based on what we know about its design, this approach seems to be tackling those varied tasks by framing them as conditional video translation problems where the goal is translating a source video Vs into a target video Vt conditioned on an instruction c <ref:2311.18837#pg0>.

Tom: Exactly, Jane. It really suggests that we can use one unified architecture instead of needing separate models for every single editing job, which could streamline how we approach these complex visual tasks.

Jane: And the core idea seems to be leveraging diffusion models to achieve convincing generative results for diverse input videos and written instructions both qualitatively and quantitatively <ref:2311.18837#pg1>.

Lu: I'm particularly intrigued by the architecture they propose, which is built upon a Latent Diffusion Model adapted specifically for video translation <ref:2311.18837#pg0>. They use a modified U-Net with four downsample/upsample blocks and one middle block, inflated into three dee convolutions to deal with the video input <ref:2311.18837#pg0>.

Meng: Three-dimensional convolutions for video inputs certainly sound computationally intensive; I need to know how they manage that in terms of processing time on real hardware.

Lalam: The paper details a multi-stage training method where they first train the original Text-to-Image model, and then introduce the temporal attention layer while inflating the U-Net from 2D to three dee for Video-to-Video generation with a video-text dataset <ref:2311.18837#pg0>.

Jane: That multi-stage training sounds like a clever way to smoothly transfer knowledge from an existing Text-to-Image model into this video translation capability <ref:2311.18837#pg0>. It’s about taking something already trained and tuning it for a new, more complex task.

Tom: Right, and that leads us to how it handles the instructions themselves, which I think is where the real innovation lies for making it truly multimodal.

Lu: They introduce a straightforward multi-modal condition injection mechanism for image and text-guided video editing <ref:2311.18837#pg0>. For a textual instruction, they use the CLIP-Text encoder to extract embeddings, and they also incorporate images as visual instructions <ref:2311.18837#pg0>.

Meng: So they are combining those two types of input—text and image—into a single joint instruction embedding by concatenating the image embedding and text embedding along the channel dimension <ref:2311.18837#pg0>. That sounds like a solid way to ensure both modalities influence the generation process simultaneously.

Paper summary: Lalam: From my perspective as an LLM, I see this capability to ingest and fuse different types of guidance—textual descriptions and visual examples—as highly beneficial for improving cultural understanding in how we generate content <ref:2311.18837#pg0>. It allows the model to grasp not just *what* to change, but *how* it should look visually based on an example.

Tom: It’s impressive how they manage to keep the CLIP vision and text encoders fixed while only training a new MLP layer for this joint instruction embedding <ref:2311.18837#pg0>. That’s efficient design thinking right there, balancing flexibility with focused training on the specific video task.

Jane: And the way they frame all these different video understanding tasks—re-colorization, deblurring, etc.—as conditional video translation problems really ties the whole framework together <ref:2311.18837#pg0>.

Lu: They construct triplet data for each task; for instance, for enhancement tasks like dehazing and deblurring, they use established datasets combined with manual instruction phrases such as “remove the applied haze from this video” <ref:2311.18837#pg0>. This shows a pragmatic approach to building the training set.

Meng: Pragmatic is good, but I wonder how robust their iterative inference pipeline is when dealing with very long videos; consistency across those many clips is always a tricky engineering hurdle <ref:2311.18837#pg0>. They mention replacing the initial n frames of the source video with frames from the preceding clip number one for subsequent clips, which sounds like a clever way to maintain continuity.

Lalam: That iterative generation method addresses length concerns by ensuring that each new segment builds directly upon what came before, which is vital for maintaining coherence across long sequences <ref:2311.18837#pg0>. It's about building context sequentially rather than generating everything from scratch at once.

Tom: So, we've talked about the core idea of VIDiff—a generalist diffusion framework that handles various video translation tasks using multi-modal instructions <ref:2311.18837#pg0>. Now we need to step back and think about what this actually means for the future of video content creation.

Jane: I think the paper’s focus on unifying these diverse tasks under one model framework really suggests that we might see a significant simplification in how creators approach video editing in the future <ref:2311.18837#pg0>. It moves us away from needing specialized tools for every single visual correction.

Lu: If this unified framework proves effective, it could open up entirely new creative avenues where complex edits are no longer limited by the specific architecture of a single editing tool <ref:2311.18837#pg2>. We could envision more intuitive, instruction-based workflows that feel more natural for human creators.

Meng: From an engineering standpoint, if the model can handle arbitrary lengths with that iterative approach, it makes it much more viable for production environments where videos aren't always perfectly stitched together <ref:2311.18837#pg0>. That practical scalability is something I’d look at closely.

Lalam: For culture, this means that the barrier to entry for sophisticated video manipulation drops substantially; people who can articulate their vision through instruction, whether text or image-guided, can achieve results previously requiring specialized technical skills <ref:2311.18837#pg0>. It democratizes complex visual editing.

Paper summary: Tom: That’s a big picture thought, Jane. So, to wrap up on the paper "VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models," the authors have presented a unified diffusion framework designed to accomplish a wide range of video translation tasks using both textual and image instructions <ref:2311.18837#pg0>.

Jane: And their work on the multi-stage training method shows how they successfully transfer knowledge from an existing model into this new instruction-based video editing capability <ref:2311.18837#pg0>. The implications are that we have a single, flexible tool for many different visual enhancements and edits, which is quite something <ref:2311.18837#pg0>.

Lu: The way they handle the multimodal condition injection mechanism, by concatenating image and text embeddings into a joint instruction embedding, is a clever way to ensure both inputs have equal weight in guiding the diffusion process <ref:2311.18837#pg0>. That's sophisticated conditioning.

Meng: I still wonder about the practical limitations mentioned; they do note that for language-guided object segmentation, they use established datasets with specific instructions, which implies that perfect segmentation might still require very high-quality training data tailored to those specific tasks <ref:2311.18837#pg0>.

Lalam: That is a fair point; even with this unified model, the quality of the input instruction and the training data for specific granular tasks will always dictate how accurate the output becomes <ref:2311.18837#pg0>. The model is powerful, but it’s still tied to its training experience.

Tom: So while VIDiff shows strong performance across benchmarks, like achieving a CLIP Score of thirty-one point one five and a PickScore of twenty point seven three in video editing experiments <ref:2311.18837#pg0>, the real excitement is seeing how this unified approach can be applied broadly rather than just on isolated tasks.

Jane: It really suggests that the future involves models that don't need to be specialized for every single visual manipulation; instead, they handle a broad spectrum of video editing requests using a consistent underlying structure <ref:2311.18837#pg0>. That’s the core idea behind this paper.

Lu: It’s fascinating because it tackles the inherent complexity of video translation by treating it as a unified problem, which is much harder than just tackling individual tasks sequentially <ref:2311.18837#pg2>. We're moving toward systems that understand the entire editing request at once.

Meng: If we can get this architecture running reliably on standard hardware without massive overhead, it could significantly impact how video production pipelines are structured in the industry <ref:2311.18837#pg0>. That kind of efficiency is what engineers look for.

Lalam: For cultural impact, imagine creating personalized learning materials or complex visual narratives where the creator just describes what they see and want to change, without needing to know the underlying diffusion mechanics <ref:2311.18837#pg0>. It empowers creators immensely.

Tom: We’ve covered a lot on VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models, from its thesis on unifying video tasks to the practical considerations of its architecture and future creative possibilities. That’s our rundown for today's discussion.

Conclusion: Tom: So, we've seen how VIDiff tackles things like re-colorization and deblurring using this unified diffusion framework, now we're wrapping up by talking about what this paper is actually called and who wrote it, and what it means for us all.

Jane: That’s right, Tom. We’re looking at the title "VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models" and who the researchers behind this work are, which really helps us understand the core concept of this model.

Lu: The authors are bringing together diffusion models and multi-modal conditioning to tackle a broad set of video translation problems, which is a very ambitious scope for any single model.

Meng: From my side, I’m thinking about how this unified approach could simplify the development pipeline for AI startups trying to build production-ready video editing tools.

Lalam: For me, what this paper shows is that we can move closer to a future where creating complex visual content just means describing what you want in plain language or with an image guide, which really democratizes sophisticated creative work.

Tom: Exactly, Lu! That unified framework is the big idea here—it means one architecture can handle so many different types of video translation tasks simultaneously.

Jane: And it’s important to remember that this research came from a team of experts who have been pushing the boundaries in generative AI for a long time.

Lu: Their work really focuses on creating a system that isn't just good at one thing, but can adapt its understanding based on different kinds of instructions, which is what makes it so interesting.

Meng: So, if we boil it down, the main point is that they’ve built a diffusion model specifically designed to be flexible enough to handle multiple video editing jobs with text and image guidance.

Lalam: And that flexibility is key; it means the tool can follow a creator’s nuanced vision whether they describe it verbally or show an example visually.

Tom: It really boils down to this paper showing us a unified way to approach video translation problems, moving away from needing separate specialized models for every single edit.

Jane: And this unification is what makes VIDiff so significant because it offers a consistent foundation for handling diverse visual tasks under one umbrella.

Lu: This sets up an exciting direction where we can explore how these multi-modal instruction methods can be integrated into more complex, long-form video generation systems next.

Zhen Xing, Qi Dai, Zihao Zhang, Hui Zhang, Han Hu, Zuxuan Wu

Fudan University · Microsoft Research Asia

cs.CV, cs.AI, cs.LG, cs.MM

Submitted: 2023-11-30

Updated: 2026-10-02

Project page: https://chenhsing.github.io/VIDiff

Importance score: 81/100

The gist: Diffusion models have achieved significant success in image and video generation, motivating research into video editing tasks guided by natural language instructions.

Key concepts

Latent Diffusion Model (LDM)
This is the core architecture used to generate the video translations. It works by iteratively refining random noise into a coherent video output within a compressed, lower-dimensional latent space rather than directly in pixel space. This makes the generation process more efficient and stable for complex tasks like video editing.
Multi-Modal Condition Injection
This mechanism allows the model to understand instructions given in both text and images simultaneously. Text is processed by a CLIP encoder, while an image instruction (derived from a frame) is processed by another CLIP vision encoder. These two embeddings are combined to form a joint instruction embedding that guides the video translation process.
Iterative Inference Approach
For translating long videos, this method ensures consistency across all clips. After generating the first clip, subsequent clips are generated by replacing the initial frames of the source video with frames from the previously generated clip. This overlapping and iterative process maintains temporal coherence over extended sequences.
Conditional Video Translation
VIDiff treats every video task as a translation problem where a source video is transformed into a target video based on an instruction. The goal is to learn how to translate the input video ($V_s$) into a desired output ($V_t$) conditioned on the specific instruction ($c$), such as 'remove haze' or 'change style'.

Terminology

Summary

Diffusion models have achieved significant success in image and video generation, motivating research into video editing tasks guided by natural language instructions. This paper introduces Video Instruction Diffusion (VIDiff), a unified foundation model designed to effectively accomplish a wide range of video translation tasks—including re-colorization, deblurring, inpainting, and style transfer—by accepting both source videos and multimodal instructions (text and images).

The gist

VIDiff is a generalist model for video translation tasks that effectively accomplishes tasks such as video re-colorization, dahazing, deblurring, editing, in-painting, and object segmentation given an input video and human instructions.

Model Architecture and Training Stages

VIDiff is built upon a Latent Diffusion Model (LDM) adapted for video translation. The core architecture incorporates a modified U-Net comprising 4 downsample/upsample blocks and 1 middle block, which is inflated into 3D convolutions to cope with video inputs. Crucially, it adds a vanilla temporal attention layer for motion modeling. The model utilizes the pre-trained auto-encoder from Stable Diffusion [44] to obtain latent representations.

The training process employs a multi-stage training method to seamlessly transfer a pre-trained Text-to-Image (T2I) model for Video-to-Video (V2V) translation.

  1. The first stage is the original T2I model training [44].

  2. In the second stage, the temporal attention layer is introduced, and the U-Net is inflated from 2D to 3D, tuning it to achieve T2V generation with a video-text dataset [4].

  3. The final stage fine-tunes the pre-trained network using collected datasets to accomplish the video-to-video translation task.

Multi-Modal Condition Injection Mechanism

The model is designed to handle multimodal instructions by employing a straightforward multi-modal condition injection mechanism for image and text-guided video editing. For a given textual instruction, the CLIP-Text encoder extracts the text embedding. Additionally, images are incorporated as visual instructions; during training, a frame from the target video is randomly selected and augmented (flipping, rotation, cropping) to create an image instruction. This image instruction is processed through a pre-trained CLIP-Vision encoder and a newly added MLP layer. The resulting image embedding and text embedding are then concatenated along the channel dimension to form a joint instruction embedding, allowing the MLP layer to be trained while keeping the CLIP vision and text encoders fixed.

Unified Instructional Framework for Video Tasks

VIDiff treats various video understanding tasks as conditional video translation problems, where the objective is to translate a source video Vs into a target video Vt conditioned on an instruction c. The training data construction involves creating triplets for each task:

  1. For re-colorization and inpainting, unlabeled videos can be converted to grayscale or masked for missing parts, with instructions like “convert the grayscale clip into a colorful masterpiece” or “repair the video with missing parts.”

  2. For enhancement tasks like dehazing and deblurring, established datasets are utilized with manual instruction phrases such as “remove the applied haze from this video” or “enhance the clarity of this blurry video.”

  3. For language-guided object segmentation, established datasets are used with instructions like “Mark the pixels of the moving car to Green and leave the rest unchanged.”

  4. For editing tasks, triplet data is created by following [6, 40] using GPT and advanced video editing models.

Inference Pipeline for Long Video Translation

The model employs an iterative inference approach for long videos to maintain consistency. For the first clip, a regular inference approach is used based on the source video and instructions as conditions. Once the first clip is obtained, subsequent clips are translated iteratively: For the next clip number 2, we replace the initial n frames of the source video with the corresponding frames from the preceding clip number 1. This overlapping sampling and iterative inference method ensures consistency in the translation of videos with arbitrary lengths. The model also leverages information from reference frames when computing temporal attention to maintain coherence across clips.

Key Contributions and Results

The main contributions include:

We are the first to employ a unified diffusion framework for both video understanding and video enhancement tasks.

We design a multi-stage training method to seamlessly transfer the T2I model for multi-modal conditional video translation tasks.

Our proposed iterative generation method is simple yet effective, allowing easy application in long video translation tasks.

Experiments demonstrate that VIDiff outperforms existing methods across multiple benchmarks. In video editing, VIDiff achieved a CLIP Score of 31.15 and a PickScore of 20.73, surpassing baselines like Tune-A-Video [57] (CLIP Score 30.

Improvements for AI systems

Here are specific improvements that could be made to existing AI systems by leveraging the capabilities of VIDiff, and what those improved systems could achieve:

  1. The core improvement is transitioning from task-specific video models to a single, unified foundation model capable of handling a vast spectrum of video operations (understanding, enhancement, editing) via natural language instructions.

  2. The system can perform complex multi-modal instruction following by accepting both text and image inputs as conditioning signals simultaneously.

  3. The improved system can execute diverse video translation tasks in seconds with minimal or no per-video training/tuning, overcoming the major bottleneck of existing methods that require time-consuming inference or extensive fine-tuning (like DDIM inversion).

Specific capabilities of the improved AI System:

  1. The system can perform high-fidelity video re-colorization and style transfer (e.g., transforming a video into Oil Painting Style or Van Gogh Night Style) by interpreting complex stylistic instructions derived from both text and reference images.

  2. It can execute precise, localized object manipulation guided by spatial instructions (e.g., Apply Green to the pixels of the cat playing with the teaser while maintaining the current state of other pixels), enabling fine-grained control over specific regions within a video.

  3. The system can perform content restoration and enhancement tasks, such as clearing haze or removing blur from low-definition footage, achieving superior perceptual quality metrics (as demonstrated by improved FID/PSNR scores compared to baselines).

  4. It can execute complex generative editing tasks like inpainting missing video segments based on textual descriptions of the desired content.

  5. The system can maintain temporal consistency across long video sequences, ensuring that edits or enhancements applied to one clip are coherent with preceding and succeeding clips (Long Video Translation), which is currently a major limitation for most diffusion-based video models.

  6. The system can be trained via a multi-task learning paradigm, allowing it to generalize better across different video modalities and tasks than specialized single-task models, leading to superior overall performance on unseen or complex combinations of instructions.

Sources

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