Structure and Content-Guided Video Synthesis with Diffusion Models (2302.03011v1)

Abstract: 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.

• The paper introduces a novel video diffusion model that separates structural dynamics from content semantics to achieve precise video editing.
• The methodology leverages depth map guidance and pretrained content embeddings for smooth temporal coherence and detailed visual transformations.
• The model offers user control via classifier-free guidance and fine-tuning, enabling tailored style modifications without retraining.

Introduction to Video Diffusion Models

Diffusion models have established themselves as powerful tools in the field of generative image models. They are lauded for their ability to create intricate and aesthetically pleasing visuals from textual descriptions or sets of images. This paradigm is now beginning to transform video editing, offering a method to edit and synthesize videos in a novel and efficient way. Traditional techniques in video editing have been hampered by the inherent complexity of video as a medium, due to its time-based structure.

From Images to Videos

The emergence of diffusion models in image synthesis has now transitioned toward video generation. By adapting image models to accommodate the temporality of video data, significant breakthroughs have been made in video editing. This model leverages the capability of latent video diffusion techniques, allowing it to edit videos according to specified visual or textual edits without necessitating retraining for each new input or relying on the less accurate method of propagating image modifications across frames.

A Novel Structure and Content-Aware Approach

At the core of this model lies the distinction between content and structure. The structure refers to the geometric and temporal dynamics of a video, such as shapes and movements, while content pertains to appearance, color, style, and semantics. The model ingeniously intertwines these aspects, providing a means to edit videos while retaining the original video's structure. For instance, it employs depth maps to create a representation of video structure, ensuring geometric continuity in the generated clips. The content, alternatively, is represented using embeddings from a neural network pre-trained on a large dataset.

This dual representation endows the model with remarkable editing capabilities, ranging from radical transformations like turning a summer daytime scene into a wintry evening, to subtly altering aesthetics such as changing the style from live-action to clay animation. The model's effectiveness is further boosted as it is trained jointly on both image and video data, which bestows explicit control over temporal consistency to the user.

User Control and Model Customization

The model introduces a novel guidance system inspired by classifier-free guidance. This system confers the user control over the temporal consistency of generated videos, enabling smooth transitions and coherence in temporal details. Additionally, the model's training procedure on depth maps at varying levels of detail allows the user to fine-tune the degree of structural fidelity during the editing process.

Interestingly, the model presents the advantage of customization. Users can fine-tune the pre-trained model on a small dataset of images to craft tailor-made videos centered around specific subjects or styles. This customization feature bridges the gap between generalized generative models and specialized video production needs.

Conclusion

This structure and content-aware video diffusion model stands as a significant leap forward in the domain of video editing. It is not merely about transplanting generative models designed for images onto videos; it's about innovatively and discerningly adapting and extending their capabilities to the video format. This adaptation provides a user-friendly tool that respects the video medium's complexities while offering artistic freedom and precision editing, making it an enticing proposition for professionals and hobbyists alike. Moving forward, this model can inspire further research into more specialized content representations and integrations with 3D modeling techniques, paving the way for even more dynamic and realistic video editing experiences.