CustomCrafter - A custom video generation framework jointly developed by Tencent and Zhejiang University

What is CustomCrafter?

CustomCrafter is a custom video generation framework jointly proposed by Tencent and Zhejiang University. It can generate high-quality, personalized videos based on text prompts and reference images, while retaining motion generation and concept composition capabilities. Through a series of flexible modules, CustomCrafter enables the generation of desired videos without additional video content, using only a small amount of image learning. CustomCrafter supports custom subject identities and motion patterns, generating videos with text prompts by preserving motion generation and concept composition capabilities.

Main functions of CustomCrafter

• Text prompts and reference images generate videoCustomCrafter can generate videos based on user-provided text prompts and reference images, meaning users can specify the content and style of the videos they want to generate.
• Retaining motion generation abilityThe framework preserves the continuity and fluidity of motion when generating videos, producing videos with natural motion even without additional video guidance.
• Concept combination abilityCustomCrafter allows you to combine different concepts to create creative and diverse video content.
• Learning from a small number of imagesThe framework design allows the model to learn from a small number of images without requiring a large amount of video data, reducing the complexity of data collection and processing.
• Spatial Theme Learning ModuleCustomCrafter uses the LoRA method to build a spatial theme learning module that updates the attention layer parameters in the spatial transformer model to better capture appearance details.

CustomCrafter's technical principles

• Video Diffusion Model (VDM)CustomCrafter generates video based on a video diffusion model. VDM is a generative model that generates data, specifically video frames, by progressively removing noise.
• Spatial Subject Learning ModuleTo enhance the model's ability to capture the appearance details of new subjects, CustomCrafter designed a plug-and-play module. This module achieves this by updating the Query, Key, and Value parameters of the attention layer in the spatial transformer model.
• Dynamic Weighted Video Sampling StrategyCustomCrafter observed that VDM tends to recover video motion in the early stages of denoising, while focusing on recovering subject details in the later stages. Based on this observation, CustomCrafter proposes a strategy to reduce the influence of the spatial subject learning module in the early stages of denoising to preserve VDM's ability to generate motion; and to increase the influence of this module in the later stages of denoising to restore the appearance details of specified subjects.
• Two stages of the noise reduction processCustomCrafter divides the denoising process into two stages: motion layout repair and theme appearance repair. In the motion layout repair stage, the influence of the spatial theme learning module is reduced to maintain motion continuity; in the theme appearance repair stage, the influence of this module is restored to ensure the realism of the subject's appearance.

CustomCrafter's project address

• GitHub repository:https://github.com/customcrafter
• Project Homepage:https://customcrafter.github.io/
• arXiv technical paper:https://arxiv.org/pdf/2408.13239v1

How to use CustomCrafter

• Environmental preparationEnsure that all necessary software and libraries are installed in the computing environment, such as Python, deep learning frameworks (such as PyTorch or TensorFlow), etc.
• Get CustomCrafterVisit CustomCrafter's GitHub repository orProject HomepageDownload or clone the code repository to your local machine.
• Install dependenciesAccording to the project requirements.txt or setup.py The file requires the installation of necessary dependency packages.
• Data preparationPrepare or collect text prompts and reference images for video generation. This data will serve as input to the model.
• Configuration parametersConfigure model parameters according to the type and style of the video to be generated, including but not limited to text prompts, reference image paths, output video resolution and frame rate, etc.
• Model training(If needed): If CustomCrafter needs to be trained or fine-tuned to fit a specific dataset or generation task, train the model according to the instructions in the project documentation.
• Video generationUse the configured parameters and input data to run CustomCrafter's video generation script or command-line tool to generate a video.

Application scenarios of CustomCrafter

• Film and television productionIn film, television series, or short film production, CustomCrafter can be used to quickly generate animations or special effects for specific scenes, reducing the cost and time of actual shooting.
• Virtual Reality (VR) and Augmented Reality (AR)In VR and AR applications, CustomCrafter can generate realistic virtual characters or environments, providing an immersive experience.
• Game developmentGame designers can use CustomCrafter to generate dynamic scenes or character animations in games, improving visual effects and player experience.
• Advertising and MarketingMarketers can use CustomCrafter to quickly generate engaging video ads tailored to different markets and audiences.
• Social media content creationSocial media users can use CustomCrafter to generate unique video content, increasing fan engagement and content appeal.