Controllable Video Generation via Adapter Modules
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Solution Overview
Problem
Existing video generation systems using neural networks struggle to generate long, multi-scene videos from text prompts while maintaining temporal coherence and relevance, especially when additional control inputs like images or location data are not considered.
Innovation Solution
A controllable video generation system that uses a combination of a joint encoder neural network and a video generation neural network, with adaptive cross-attention layers, to condition video generation on both text prompts and additional control inputs such as images and location data, allowing for greater user control over video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a pre-trained text-to-video model is used to generate videos from text prompts, then video generation capability is provided, but the system lacks controllability over specific objects and their appearances
Solution Approach 1:
The patent embeds trainable adapter modules (cross-attention layers and MLPs) within the frozen pre-trained transformer blocks. These adapter modules are nested inside the existing model architecture, allowing the system to gain object controllability through compact inserted components rather than redesigning the entire model.
Solution Approach 2:
The patent segments the video generation task by introducing separate adapter modules for different objects. Each object can be controlled independently through its own embedding and adapter parameters, allowing fine-grained control over specific objects while keeping the rest of the model frozen.
2Adaptability or versatility
If control inputs like images and location data are added to enhance video controllability, then user control over video content improves, but computational resource usage increases
Solution Approach 1:
The patent performs preliminary encoding of control inputs (images, location data, text prompts) into embeddings using frozen encoder modules before the main generation process. This preliminary processing organizes the control information in advance, reducing the computational burden during the actual video generation steps.
Solution Approach 2:
The patent applies control inputs locally to specific objects rather than globally to the entire video. The cross-attention layers and adapter modules are object-specific, allowing control information to be processed only where needed, reducing unnecessary computational overhead.
3Duration of action of moving object
If existing text-to-video generation techniques are used, then video generation is possible, but temporal coherence and relevance in long multi-scene videos deteriorate
Solution Approach 1:
The patent incorporates attention mechanisms that allow the model to attend to and maintain consistency with previously generated frames and control inputs throughout the video generation process. This feedback loop ensures temporal coherence by continuously referencing earlier states and control specifications.
Solution Approach 2:
The patent uses a unified transformer-based architecture with adapter modules that can handle multiple objects, scenes, and control input types within a single model framework. This universal approach maintains consistency across diverse multi-scene videos without requiring separate specialized models for each scene type.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controllable video generation. One of the methods includes receiving a text prompt that specifies an object; receiving a control input that comprises an image that depicts a particular instance of the object; generating a video that comprises a respective video frame at each of a plurality of time steps in the video and that depicts the particular instance of the object. Generating the video includes, at each of the plurality of time steps: obtaining a text prompt embedding; obtaining a control input embedding; and generating the respective video frame at the time step using a video generation neural network while the video generation neural network is conditioned on the text prompt embedding and on the control input embedding.