Still-Moving - DeepMind's AI video generation framework

What is Still-Moving?

Still-Moving is an AI video generation framework from DeepMind that allows users to customize text-to-video (T2V) models without requiring specific video data. By training a lightweight spatial adapter, Still-Moving adjusts the features of a T2I model on still images to match the motion characteristics of a T2V model. This approach preserves the personalization and stylization of the T2I model while incorporating the motion capabilities of the T2V model, providing an efficient way to customize videos without additional data.

The main functions of Still-Moving

• Custom video generationIt supports users in adapting personalized text-to-image (T2I) model weights to text-to-video (T2V) models.
• No need to customize video dataThe framework can be trained without specific video data, reducing the need for data collection and processing.
• Lightweight space adapterThe T2I model's features are adjusted by training an adapter to match the motion characteristics of the T2V model.
• motion adapter moduleUsed during the training phase to help the model learn how to simulate motion on still images.
• Remove the motion adapter during testing.In the final application, only the space adapter is retained to restore the original motion characteristics of the T2V model.

The technical principle of Still-Moving

• T2I Model CustomizationThe user has a custom text-to-image (T2I) model trained on static images to adapt to a specific style or content.
• Space adapter trainingTo adapt the custom weights of the T2I model to video generation, a lightweight spatial adapter is trained using Still-Moving. The adapter adjusts the features generated by the T2I layers to ensure they match the motion characteristics of the video model.
• motion adapter moduleUsed during the training phase, this module enables the model to learn motion characteristics from videos constructed from still images generated by a custom T2I model. This module helps the model understand how to incorporate motion into static images.
• Static video trainingThe adapter is trained on still videos constructed from image samples generated by a custom T2I model. The training method allows the model to learn how to simulate motion without actual motion data.
• Adapter removal during testingDuring the testing phase, the motion adapter module is removed, and only the trained spatial adapter is retained. The T2V model can recover its original motion priors while adhering to the spatial priors of the custom T2I model.
• Prior knowledge integrationThis method allows Still-Moving to seamlessly combine the personalized and stylized priors of the T2I model with the motion priors of the T2V model, generating videos that meet user customization needs while possessing natural motion characteristics.

Still-Moving project address

Applications of Still-Moving

• Personalized video productionUsers can generate video content with specific characters, styles, or scenes according to their own needs.
• Artistic CreationArtists and designers can use Still-Moving to create unique video artworks, transforming still images into dynamic videos.
• Content MarketingBusinesses and brands can use this framework to generate engaging video ads or social media content to enhance user engagement.
• Film and game productionIn film post-production or game development, Still-Moving can be used to quickly generate or edit video footage, improving production efficiency.
• Virtual Reality and Augmented RealityIn VR and AR applications, Still-Moving can generate realistic dynamic backgrounds or characters, enhancing the user experience.