Source-linked AI summary
Structure and Content-Guided Video Synthesis with Diffusion Models
Patrick Esser, Johnathan Chiu, Parmida Atighehchian, Jonathan Granskog, Anastasis Germanidis
TL;DR
Existing video-editing methods struggle to change content while preserving structure without expensive per-video training or unreliable frame propagation. This paper proposes a structure- and content-guided video diffusion model using depth, image or text conditioning, and joint image-video training with temporal guidance. The method supports diverse edits, controllable structure and temporal consistency, few-shot customization, and strong user preference over related approaches.
Problem
Current video-editing methods require expensive retraining for individual videos or error-prone propagation while balancing temporal consistency and spatial detail.
Method
The model uses monocular depth for structure, pretrained embeddings for image or text content, joint image-video training, and custom guidance for temporal-consistency control.
Results
The method performs well across diverse footage, supports reference-image character replacement and masked editing, and is preferred roughly 3 out of 4 times across compared methods.
Takeaways & Limitations
Inference-time control over structure, content, and temporal consistency enables flexible video editing and customization from a small set of reference images.
Takeaways & Limitations
Training uses uncaptioned videos because large-scale paired video-text data are unavailable, so the model lacks video-edit-prompt-output triplets and video-caption pairs.
Abstract
from arXiv · showhide
Text-guided generative diffusion models unlock powerful image creation and editing tools. While these have been extended to video generation, current approaches that edit the content of existing footage while retaining structure require expensive re-training for every input or rely on error-prone propagation of image edits across frames. In this work, we present a structure and content-guided video diffusion model that edits videos based on visual or textual descriptions of the desired output. Conflicts between user-provided content edits and structure representations occur due to insufficient disentanglement between the two aspects. As a solution, we show that training on monocular depth estimates with varying levels of detail provides control over structure and content fidelity. Our model is trained jointly on images and videos which also exposes explicit control of temporal consistency through a novel guidance method. Our experiments demonstrate a wide variety of successes; fine-grained control over output characteristics, customization based on a few reference images, and a strong user preference towards results by our model.
1. Introduction
Video editing remains difficult because temporal structure complicates intuitive, detailed edits, while existing approaches require costly per-video training or correspondence computation. The paper introduces an inference-time diffusion model that separately controls structure and content using depth, images, or text.
- Video editing tools must balance temporal consistency with spatial detail because video data has an inherently temporal format.
- Existing methods either propagate edits using explicit correspondences or finetune on each video, making arbitrary-video editing expensive.
- The proposed model represents structure with monocular depth estimates and content with embeddings from a pretrained neural network.
- Content can be specified by reference images or text prompts to control appearance and style in inferred videos.
- The model jointly trains on images and videos and provides inference-time control over temporal, content, and structure consistency.
2. Related Work
Related work spans unconditional video generation, image diffusion, style transfer, and video editing, but the paper focuses on controllable synthesis that combines content guidance with structural constraints. Its approach differs from prior editing methods by generating semantically consistent outputs without per-video training or feature-statistics matching.
- Unconditional video generation: Unconditional GAN and autoregressive methods generate videos but provide limited user control over synthesis and may face stability, duration, or artifact limitations.
- Diffusion models for image synthesis: Diffusion models improve image synthesis through better parameterizations, sampling, architectures, and conditioning such as CLIP- or T5-based text embeddings.
- Video editing: The paper’s overview combines latent diffusion with depth-based structure conditioning and CLIP-based content conditioning for controllable video synthesis.
- Video style transfer: Video style-transfer methods apply reference-image statistics, whereas this method combines style and content from text or images under extracted structural constraints.
- Video editing: Prior video-editing approaches propagate edits, optimize on one video, or finetune per video, with per-video training limiting practical use.
3. Method
The method models video generation as structure- and content-conditioned latent diffusion, extending an image architecture with temporal processing while sharing parameters across images and videos. It uses depth-based structure representations with adjustable detail, CLIP-based content representations, and inference-time guidance to control edits and temporal consistency.
- Structure and content: The model represents videos through structure and content, preserving geometry and dynamics while editing appearance, style, colors, semantics, or lighting.Structure includes shapes, locations, and temporal changes; content includes appearance and scene semantics.
- Conditional diffusion: It learns a conditional video distribution p(x|s, c), inferring structure from an input video and modifying content from a text prompt.At inference, the edited video is sampled using structure from the input video and content from the requested text description.
- Temporal architecture: Joint image-video training uses shared parameters while modeling inter-frame relationships, treating images as single-frame videos for a uniform architecture.The autoencoder processes frames independently, and temporal operations handle relationships between frames.
- Temporal architecture: The video architecture extends a shared image model with temporal layers active for video inputs, including temporal convolutions and self-attention across frames.Spatial layers remain shared, while temporal layers are inserted after spatial convolutions and attention blocks; frame-index positional encodings are added to temporal transformers.
- Representations: Monocular depth maps provide structure conditioning, while CLIP image embeddings represent content from video frames, text, or reference images.Depth maps are blurred and downsampled with a controllable level ts, and CLIP embeddings emphasize semantic and stylistic properties while remaining relatively invariant to precise geometry.
- Controllable guidance: Training on depth representations with varying information levels controls structure preservation, and temporal guidance adjusts consistency using predictions from jointly trained image and video models.The temporal guidance scale ωt controls consistency, with experiments showing that the approach controls temporal consistency in outputs.
4. Results
The model edits diverse videos using textual or image-based content guidance while preserving input structure, with controls for masking, temporal consistency, prompt consistency, and customization. It is preferred over baselines in user studies and supports broad qualitative editing capabilities.
- 4.1. Qualitative Results: The model edits diverse footage without explicit tracking, supporting style, environment, and character changes across static, shaky, landscape, and close-up videos.Examples include anime and claymation styles, day-to-sunset and summer-to-winter changes, and transforming hikers or bears into new characters.
- 4.1. Qualitative Results: Image prompts enable character replacement while retaining content characteristics with ts = 3 despite substantial pose and shape differences.The evaluation resynthesized six videos five times each using a single content image from another video.
- 4.1. Qualitative Results: Masked denoising restricts edits to selected regions, enabling background editing while retaining original content inside masked areas.The method predicts content outside the masked area and preserves the original content inside it.
- 4.2. User Study: Across 35 editing prompts, users preferred the model's results roughly 3 out of 4 times over compared methods.Five annotators compared prompt faithfulness for each baseline-model pair, using majority votes.
- 4.3. Quantitative Evaluation: The model tends to outperform baselines in both frame and prompt consistency, while temporal and structure scales expose a controllable trade-off.Increasing temporal scale raises frame consistency but lowers prompt consistency; increasing structure scale raises prompt consistency by reducing dependence on input structure.
- 4.4. Customization: Customization on 15-30 images improves style and appearance fidelity, and higher ts enables accurate animations from driving videos with different characteristics.During finetuning, half of each batch uses the custom subject and half uses the original training data to avoid overfitting.
5. Conclusion
The paper presents latent video diffusion synthesis that separates structure from content using depth guidance and image or language conditioning. Joint image-video training and temporal guidance support stable outputs, controllable structure preservation, customization, and strong user preference over related approaches.
- 5. Conclusion: The model synthesizes videos from structure and content information, using depth estimates for structural consistency and images or natural language for content control.It also supports adjusting structure preservation through depth maps with varying fidelity.
- 5. Conclusion: Temporal connections, joint image-video training, and novel guidance provide temporally stable results with user control over temporal consistency.The same training strategy also exposes control over structure preservation for customization.
- 5. Conclusion: Quantitative evaluation and a user study show that the method is highly preferred over related approaches.The conclusion reports this preference without specifying a numerical margin.
Supplementary Material
The supplementary material provides quantitative data and additional qualitative examples spanning text- and image-based edits, customization prompts, and comparisons with evaluated methods. These materials illustrate the paper's editing capabilities and evaluation setup.
- Supplementary Material: Figures S1-S7 show additional text-based video-editing results, while Figures S8-S12 show additional image-based editing results.The supplementary examples cover both primary conditioning modes.
- Supplementary Material: Figures S13 and the listed prompts provide qualitative comparison material and examples of edited video content across varied subjects, scenes, and styles.The comparison includes input, Deforum, the proposed method, SDEdit, IVS, Depth-SD, and Text2Live.
- Supplementary Material: Supplementary Table S1 contains the quantitative evaluations corresponding to Figures 6 and 7, with standard errors from 35 samples.The supplementary figures provide the associated visual results and comparisons.