Generative Video Environments with Latent Action Control
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Solution Overview
Problem
Existing neural networks struggle to generate controllable videos without ground-truth action labels, limiting the ability to create interactive and dynamic virtual environments.
Innovation Solution
A system utilizing generative neural networks, including a video encoder, decoder, and dynamics neural network, allows for frame-by-frame control of video generation through a learned latent action space, enabling interactive agents to influence video progression and content using context inputs like text, images, or sketches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If ground-truth action labels are used for training, then video generation controllability is improved, but data requirements and system complexity increase
Solution Approach 1:
The patent introduces an intermediary mapping layer between latent actions and video frame transitions. Instead of directly learning from ground-truth action labels, the system learns a latent action space that indirectly controls video generation, reducing the need for labeled data while maintaining controllability.
Solution Approach 2:
The system transforms the control parameters from ground-truth action labels to latent action representations. By changing the parameter space from explicit action categories to continuous latent vectors, the system achieves finer-grained control with less labeled data requirements.
2Adaptability or versatility
If frame-by-frame control is implemented, then interactive capability is improved, but computational complexity increases
Solution Approach 1:
The patent segments the video generation process into frame-by-frame steps, where each frame is generated conditioned on the previous frame and a latent action. This segmentation enables interactive control at each timestep while managing computational complexity through incremental generation rather than processing the entire video at once.
Solution Approach 2:
The system implements dynamic control by allowing latent actions to change at each timestep based on user input or external conditions. This dynamic adaptation enables interactive video generation where the video can respond to user actions in real-time, enhancing versatility while distributing computational load across timesteps.
3Ease of manufacture
If latent action space is learned without ground-truth labels, then data requirements are reduced, but training difficulty increases
Solution Approach 1:
The system employs self-supervised learning where the model learns latent actions by predicting future video frames from current frames and latent actions. The training signal comes from the video data itself rather than external labels, allowing the system to learn meaningful action representations without ground-truth annotations while reducing data requirements.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating controllable videos using generative neural networks.