Diffusion Model-Based Video Editing: A Survey

arXiv:2407.07111 · cs.CV, cs.AI, cs.LG, cs.MM · Submitted 2024-06-26 · Read on arXiv

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

Tom: Today's paper: "Diffusion Model-Based Video Editing: A Survey".

Jane: Input 'A' is a detailed abstract/summary of a survey paper titled "Diffusion Model-Based Video Editing," while Input 'B' is a meta-response indicating that it cannot summarize the paper because only…

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

Paper summary: Tom: We’ve covered a lot about how "Diffusion Model-Based Video Editing: A Survey" organizes this massive field, starting with the math, moving through image models, motion handling, and finally detailing the video editing translation tasks and their evaluation benchmarks. What are your final thoughts on what this whole paper is trying to tell us about where we're going in this space?

Jane: The authors of "Diffusion Model-Based Video Editing: A Survey" show that the rapid development of diffusion models has made things like getting "what you want is what you see" a reality for video. They provide a systematic review that categorizes the research, giving us a comprehensive view of the current state and pointing out where researchers need to focus their next efforts.

Lu: This paper really highlights the complexity inherent in applying these powerful models to temporal data; they make it clear that while diffusion models are incredibly versatile, tailoring them for video editing requires addressing specific challenges in motion representation and control mechanisms.

Meng: From an engineering standpoint, the implication is that we have a solid framework now—a structure to follow when trying to build a system that edits video—but the next step is figuring out which of these five classes actually gives us the best practical results for specific applications.

Lalam: I think the real impact here is clarifying what's achievable right now, showing us exactly how much progress has been made in bringing complex AI capabilities into practical video tools. It helps shape our expectations about what a diffusion model system can realistically do for creative work and content generation.

Tom: So, to wrap up, this survey doesn't just list papers; it builds a clear map of the diffusion model-based video editing landscape by giving us the tools to understand the technical progression. It sets a solid foundation for anyone looking to contribute new ideas to this area and shows us what kind of challenges remain before we can fully realize these capabilities in practical systems.

Conclusion: Tom: So, we've spent some time looking at how this survey lays out the entire map for diffusion model video editing, and now it's time to talk about what this paper actually means for us as listeners.

Jane: It’s a really big review, Tom; the title itself, "Diffusion Model-Based Video Editing: A Survey," tells us that these models are moving beyond just making static images and are becoming serious tools for manipulating video sequences.

Lu: Exactly, Jane; the authors of this paper have done a fantastic job of taking all those scattered ideas across different architectures—from basic diffusion math to complex motion representation—and organizing them into one coherent structure.

Meng: From my side, I’m focused on the practical reality; it helps me see exactly which parts of this research are ready to be integrated into production systems right now versus what’s still purely theoretical.

Lalam: The most impactful vision I get from this paper is that we're getting a unified language for video synthesis and editing, which could fundamentally change how creators interact with AI tools in the future.

Tom: That’s a powerful way to put it, Lalam; it’s not just about better filters; it’s about building new workflows. The authors really show us the breadth of what's possible when you look at everything from latent space manipulation to temporal adaptation methods like those attention feature injections.

Jane: I think what they do best is making the dense mathematical stuff accessible so people who aren't deep learning experts can actually grasp the concepts behind things like score functions and reverse diffusion sampling.

Lu: And it’s not just about accessibility; it’s about showing how these different classes of research—image generation, motion modeling, and direct editing paradigms—are all connected by a single underlying diffusion framework. That connection is what's really exciting to me.

Meng: I see the implication in terms of efficiency too; understanding these variations means we can start designing models that are targeted for specific tasks instead of trying to force one massive model to do everything poorly.

Lalam: Precisely, Meng; this survey gives us the blueprint for building more intelligent, context-aware AI systems that understand the nuances of temporal data better.

Tom: It really sets a high bar for what we expect from future video generation tools, pushing everyone to keep innovating in areas like motion representation and conditioning mechanisms.

Jane: So, as we wrap up this discussion on the paper's scope, it seems the main takeaway is that this field is maturing into a structured discipline with clear paths forward.

Tom: And that structure is exactly what’we need to follow as these technologies move from research papers into the actual tools we use every day.

Jane: Next up, we're going to look at some of the specific techniques discussed in the paper that allow models to actually perform those complex video edits we see online.

Nanyang Technological University

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

Submitted: 2024-06-26

Updated: 2026-10-06

Comments: 24 pages, 16 figures, a project related to this paper can be found at https://github.com/wenhao728/awesome-diffusion-v2v

Journal ref: International Journal of Computer Vision 134(10), 440 (2026)

DOI: 10.1007/s11263-026-03040-6

Code: https://github.com/wenhao728/awesome-diffusion-v2v

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 92/100

The gist: Input 'A' is a detailed abstract/summary of a survey paper titled "Diffusion Model-Based Video Editing," while Input 'B' is a meta-response indicating that it cannot summarize the paper because only

Key concepts

Forward Diffusion Process
This mathematical process models how data (like a video) is progressively corrupted or turned into noise over time. It starts with clean data and gradually adds random noise until it becomes pure static. Understanding this helps researchers design the reverse process to accurately reconstruct or edit the original content.
Score Function
The score function is a crucial mathematical tool used to guide the generation of new data during the reverse diffusion process. It essentially tells the model which direction to move in from noise toward a meaningful video, ensuring that generated frames retain realistic temporal coherence and quality.
Latent Diffusion Models (LDMs)
LDMs are an efficient approach where video editing happens in a compressed, lower-dimensional space called latent space. Instead of working with high-resolution pixels directly, the model manipulates these compact representations. This makes the process much faster and more computationally feasible for complex video tasks.

Terminology

Summary

Input 'A' is a detailed abstract/summary of a survey paper titled Diffusion Model-Based Video Editing, while Input 'B' is a meta-response indicating that it cannot summarize the paper because only references were provided, not the full text.

My task is to combine these summaries into one long, detailed description of the paper. I will synthesize the comprehensive information from Input 'A', treating it as the primary source material for summarization, while acknowledging the context provided by Input 'B' (i.e., that this is a survey).

Here is the detailed summary:

This paper serves as an exhaustive review and survey of diffusion model-based techniques applied to video editing, spanning both theoretical foundations and practical applications. The survey systematically categorizes the vast landscape of research into five primary classes based on their underlying technological paradigms. It is structured to provide a holistic view, moving from fundamental mathematical formulations through image generation methods, motion representation strategies, specific video editing paradigms, and finally concluding with a rigorous benchmarking framework and an outlook on future research challenges.

The paper establishes the theoretical bedrock by detailing the core mathematics underpinning diffusion processes. This includes the description of the forward diffusion process (Equation 1), which models data corruption, and the crucial reverse diffusion process (Equation 2), which is used for generation. Key mathematical concepts reviewed are:

  • Gaussian Marginal Distribution (Equation 3): Defining the underlying probabilistic structure.

  • Score Function (Equation 4): Essential for guiding the reverse process.

  • Evidence Lower Bound (ELBO) Optimization: The objective function driving model training.

  • Reverse Diffusion Sampling Formulations: Including specific sampling techniques like DDIM sampling, which are critical for efficient generation.

The survey begins by contextualizing video editing within the established domain of image diffusion models, highlighting key architectural variations:

  • Direct Generation: Standard diffusion approaches for image creation.

  • Cascaded Diffusion Models (CDMs): Techniques employed to achieve high-resolution outputs.

  • Latent Diffusion Models (LDMs): A crucial paradigm where generation occurs within a pre-trained Variational Autoencoder (VAE) latent space, allowing for computationally affordable high-resolution image synthesis.

The paper thoroughly reviews the conditioning mechanisms that guide these models, such as spatial-aligned conditions (e.g., concatenation and ControlNet), Adaptive Normalization, and advanced methods like Conditioning by Cross Attention using transformer blocks, alongside standard techniques like Classifier Guidance and Classifier-Free Guidance. Furthermore, it surveys specific editing methodologies applied at this stage, including Latent State Initialization (SDEdit), Attention Feature Injection methods (such as P2P, PnP, and MasaCtrl), and Text Inversion. Efficiency enhancements are also noted through adaptations like Low-Rank Adaptation (LoRA) and Token Merging (ToMe).

Extending image priors to the temporal domain is addressed by reviewing video generation methods. This section covers factorized 3D architectures, such as Video Diffusion Models (VDM), large-scale baselines, AnimateDiff, and Latent-Shift. A critical component discussed is Motion Representation, specifically through the use of dense optical flow, which is indispensable for performing geometric warping operations required in video manipulation.

The central theme of the survey is dedicated to Video Editing, framed fundamentally as a Video-to-Video (V2V) translation task. This involves taking a source video (x s) and a target text prompt (y) to generate an edited output video (x 0). The paper meticulously reviews the various modifications made to network architectures and training paradigms:

  • Network Modifications: Techniques focusing on Temporal Adaptation.

  • Structural Conditioning: Utilizing inputs like Depth Maps, Bounding Boxes, and Appearance Conditions.

  • Training Modifications: Employing specialized losses such as Motion-Oriented Loss and Image-Video Mixed Fine-tuning.

The survey classifies editing techniques based on their mechanism:

  1. Attention Feature Injection Methods: Divided into Inversion-Based Feature Injection (Dual-Branch) and Motion-Based Feature Injection.

  2. Diffusion Latent Manipulation: Covering both Latent Initialization (e.g., Control-A-Video) and Latent Transition (e.g., Pix2Video, Rerender), including sophisticated methods like latent space fusion and grid manipulation in RAVE.

Improvements for AI systems

Here are specific improvements for AI systems, derived from the presented survey on Diffusion Model-Based Video Editing, and what these improved systems can achieve:

)Based on Section 3 (Video Editing), Section 4 (Benchmarking), and Section 6 (Challenges and Emerging Trends).

  1. A new video editing system capable of performing what you want is what you see via a comprehensive pipeline integrating multiple control modalities.

  2. A high-throughput, low-memory inference engine for video diffusion models optimized for consumer hardware using Parameter-Efficient Adaptation techniques like LoRA and Token Merging (ToMe).

  3. A robust, temporally consistent video generation model capable of handling long-form content (hours of video) by leveraging latent state manipulation and inter-frame feature injection.

  4. An interactive, user-guided editing system allowing precise manipulation of specific objects or poses within a source video using point-based conditioning.

)Specific Capabilities of the Improved AI Systems:

  1. A system that can perform complex, multi-faceted video edits in a single prompt (e.g., Replace the foreground dog with a cat, change the style to watercolor, and composite it into a landscape background).

  2. An inference engine capable of generating high-quality video edits in real-time or near real-time on standard GPUs without requiring massive pre-training or huge VRAM consumption.

  3. A generative model that can maintain semantic and structural coherence across long sequences, ensuring that edits applied to the first frame are consistently and plausibly propagated throughout the entire duration of a multi-minute video.

  4. An interactive editing interface where a user can click on a specific object in Frame 1 and drag it to a new location in Frame 100, with the system automatically tracking the object's trajectory across all frames while preserving its identity and pose consistency relative to other elements.

Abstract

The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making "what you want is what you see" a reality. Among these, video editing has gained substantial attention and seen a swift rise in research activity, necessitating a comprehensive and systematic review of the existing literature. This paper reviews diffusion model-based video editing techniques, including theoretical foundations and practical applications. We begin by overviewing the mathematical formulation and image domain's key methods. Subsequently, we categorize video editing approaches by the inherent connections of their core technologies, depicting evolutionary trajectory. This paper also dives into novel applications, including point-based editing and pose-guided human video editing. Additionally, we present a comprehensive comparison using our newly introduced V2VBench. Building on the progress achieved to date, the paper concludes with ongoing challenges and potential directions for future research.

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