Generative Model Video Generation Via Conditioning Parameters
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Solution Overview
Problem
Current rendering engines struggle to provide immersive and dynamic video representations of locations in response to user queries, lacking the ability to accurately depict scenes with specific conditions such as time, weather, and crowd levels.
Innovation Solution
A computer platform utilizing a generative machine-learned model, such as a neural radiance field (NeRF), to generate videos of locations based on user queries. The platform receives queries, generates conditioning parameters, and uses these parameters to create immersive videos that accurately depict scenes with specified conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional rendering engines are used to create 3D scenes, then real-time viewpoint changes can be achieved, but the ability to accurately depict scenes with specific conditions (time, weather, crowd levels) is insufficient
Solution Approach 1:
The patent introduces conditioning parameters as an intermediary between user queries and the generative model. These parameters (capturing time, weather, crowd levels) mediate the translation of natural language queries into accurate scene representations, enabling the system to depict specific conditions reliably
Solution Approach 2:
The system changes the parameters of the generative model by conditioning it on specific parameters extracted from user queries. By adjusting these conditioning parameters (such as time of day, weather conditions, crowd density), the model can accurately represent different scene conditions while maintaining reliability
2Use of energy by moving object
If generative machine-learned models are used to create videos, then computational resources are saved compared to traditional rendering engines, but the complexity of generating accurate scene representations increases
Solution Approach 1:
The system performs preliminary action by pre-processing user queries to extract conditioning parameters before generating the video. This preliminary extraction and structuring of parameters simplifies the subsequent generation process, reducing the overall complexity while maintaining computational efficiency
Solution Approach 2:
The patent segments the video generation process into distinct stages: query processing, conditioning parameter extraction, model conditioning, and video generation. This segmentation breaks down the complex task into manageable components, reducing system complexity while preserving resource efficiency
Data Source
AI summary
A computer platform for generating a video includes one or more memories to store instructions and one or more processors to execute the instructions to perform operations, the operations including: receiving a query from a user relating to a location; in response to receiving the query, generating conditioning parameters based at least in part on the query, wherein the conditioning parameters provide values for one or more conditions associated with a scene to be rendered at the location; generating, using a generative machine-learned model, the video, wherein the video depicts the scene at the location and with the values for the one or more conditions; and providing the video for presentation to the user.