Neural Rasterizer for Photorealistic Human Image Generation
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Solution Overview
Problem
Traditional graphics pipelines for rendering human bodies and faces lack flexibility in controlling pose, lighting, and camera view, making them unsuitable for generating consistent and realistic animations, while neural-based approaches struggle with photorealism and control over the generative process.
Innovation Solution
A neural rasterizer is trained to translate a sparse set of 3D points directly into photorealistic images in pixel space, allowing for parameterized control over 3D body shape, pose, and camera position without relying on traditional 3D rendering pipelines, using a deep learning framework that incorporates sparse 3D mesh vertices and depth information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional graphics pipelines are used for rendering human bodies and faces, then photorealism can be achieved, but the system becomes complex and requires significant human input from animators
Solution Approach 1:
The patent replaces traditional mechanical 3D rendering pipelines with a neural network-based system. The neural rasterizer learns to render images directly from sparse 3D points and control parameters, substituting the complex mechanical rendering process with a trained neural model that achieves photorealism while being controlled through simple parameter adjustments.
Solution Approach 2:
The system uses parameterized control where a small set of parameters (pose, shape, camera position, lighting) controls the generated images. By changing these parameters, the system can generate different views and animations without complex manual animation, maintaining photorealism while simplifying control.
2Adaptability or versatility
If neural-based approaches are used for image generation, then flexibility in control is improved, but photorealism and consistency in animation are degraded
Solution Approach 1:
The neural rasterizer is designed to be universal, handling multiple functions including rendering different poses, shapes, camera views, and lighting conditions within a single model. This multi-functionality allows the system to maintain photorealism across diverse scenarios while being controlled through a unified parameter interface.
Solution Approach 2:
The neural rasterizer is pre-trained on large datasets to learn photorealistic rendering patterns before deployment. This preliminary training action enables the model to generate realistic images without requiring complex real-time computations, achieving both photorealism and control flexibility during actual use.
3Manufacturing precision
If traditional mesh-based rendering methods are used, then detailed shape and reflectance can be captured, but handling complex topology and high-frequency geometry becomes difficult
Solution Approach 1:
The patent extracts only the essential 3D information needed for rendering, representing complex geometry as sparse 3D points rather than full mesh structures. This extraction removes the complexity of handling detailed topology and high-frequency geometry while retaining sufficient information for photorealistic rendering through the neural network's learned patterns.
Data Source
AI summary
Systems and methods are provided for generating an image of a posed human figure or other subject using a neural network that is trained to translate a set of points to realistic images by reconstructing projected surfaces directly in the pixel space or image space. Input to the image generation process may include parameterized control features, such as body shape parameters, pose parameters and/or a virtual camera position. These input parameters may be applied to a three-dimensional model that is used to generate the set of points, such as a sparsely populated image of color and depth information at vertices of the three-dimensional model, before additional image generation occurs directly in the image space. The visual appearance or identity of the synthesized human in successive output images may remain consistent, such that the output is both controllable and predictable.