Neural Network Motion Generation Using Segmented Models
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Solution Overview
Problem
Current methods for generating image data that combine aspects from multiple images, such as modeling people or objects in motion, often require person-specific models that are difficult to train or lack fine detail, and fail to generalize effectively.
Innovation Solution
A system that uses a combination of neural networks and 3D models, such as the SMPL model, to generate images of a target person in specific poses or motions by processing reference images and incorporating texture information, allowing for the transfer of pose and motion data to create realistic and detailed representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If person-specific models are used to generate images of people in motion, then fine detail and realism are improved, but model training difficulty and complexity increase significantly
Solution Approach 1:
The model is segmented into two distinct components: a person-agnostic motion model that captures general motion patterns, and a person-specific texture model that handles appearance details. This segmentation allows the motion generation to be generalized across different people while maintaining fine detail through separate texture processing, thereby reducing overall model complexity and training difficulty.
Solution Approach 2:
The person-agnostic motion model serves as a universal component that can be applied to generate motions for any person without retraining. This multi-functional approach allows the same motion model to work across different individuals, eliminating the need for person-specific motion modeling and significantly reducing training complexity while maintaining fine detail through person-specific texture inputs.
2Manufacturing precision
If person-specific models are used to generate images of people in motion, then realistic representations are improved, but generalizability across different people deteriorates
Solution Approach 1:
By separating the modeling into person-agnostic motion components and person-specific texture components, the system achieves both generalizability and realism. The motion model generalizes across people while the texture model preserves person-specific realism, allowing the system to generate realistic representations for any person without retraining the motion component.
Solution Approach 2:
The person-agnostic motion model acts as an intermediary that translates between different people's appearances and universal motion patterns. It mediates between the input image of a specific person and the generated motion sequence, enabling generalizability while maintaining realism through the integration of person-specific texture information from the input image.
3Manufacturing precision
If existing methods are used to model people in motion, then some level of detail is achieved, but the ability to generalize to different people and scenarios fails
Solution Approach 1:
The person-agnostic motion model provides universal applicability across different people and scenarios. It can process motion generation for any person without retraining, while the integration of person-specific texture information from input images maintains fine detail. This universal approach enables the system to generalize to different people and scenarios while preserving detailed realistic representations.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to generate one or more images. In at least one embodiment, one or more neural networks are used to generate one or more images of one or more objects based, at least in part, on a model of the one or more objects and texture information.