Controllable Image Generation With Factorized Latent Attributes
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Solution Overview
Problem
Existing rendering techniques struggle to generate realistic human faces with detailed control over attributes without the need for expensive 3D assets, and generative adversarial networks (GANs) lack the ability to generalize to out-of-distribution combinations of control parameters.
Innovation Solution
A neural renderer using a two-encoder and decoder architecture that learns a factorized latent space from both real and synthetic images, enabling explicit control over attributes such as facial features and pose, without requiring detailed hand-labeled datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional rendering techniques are used to generate realistic human faces with detailed control over attributes, then manufacturing precision of facial attributes is improved, but device complexity and cost increase due to requirement for expensive 3D assets
Solution Approach 1:
The patent uses 2D image data to create a neural rendering system that generates 3D-like images without requiring actual 3D assets. The neural network learns from 2D images and synthesizes realistic faces with detailed attribute control by copying and transforming 2D visual information into controlled 3D representations, eliminating the need for expensive 3D modeling resources
Solution Approach 2:
The patent replaces traditional mechanical rendering pipelines that require hand-crafted 3D assets with a neural network-based system. The neural network substitutes for the complex mechanical process of 3D asset creation and manipulation, using learned representations to generate images with detailed attribute control directly from 2D training data
2Ease of manufacture
If GANs are used to generate realistic faces without 3D assets, then ease of manufacture is improved, but manufacturing precision of attribute control deteriorates due to inability to generalize to out-of-distribution combinations
Solution Approach 1:
The patent segments the facial attributes into distinct controllable parameters such as pose, expression, lighting, and demographic characteristics. By organizing the latent space into separable attribute dimensions, the system can independently control each attribute and generate out-of-distribution combinations that maintain realism and precision
Solution Approach 2:
The patent transitions from 2D image generation to 3D latent space representation, adding a dimensional layer that enables explicit control over spatial attributes like pose and depth. This 3D latent dimension allows the system to generalize to out-of-distribution attribute combinations while maintaining manufacturing precision
3Manufacturing precision
If conditional models are trained with detailed labels to control GAN generation, then manufacturing precision of attribute control is improved, but adaptability to out-of-distribution combinations deteriorates
Solution Approach 1:
The patent creates a universal neural rendering system that can generate a wide variety of facial attributes and combinations through a single trained model. The model learns a general representation of facial geometry and appearance that can be manipulated to produce both in-distribution and out-of-distribution attribute combinations, making the system adaptable to diverse scenarios
Data Source
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AI summary
In various examples there is a method of image processing comprising: storing a real image of an object in memory, the object being a specified type of object. The method involves computing, using a first encoder, a factorized embedding of the real image. The method receives a value of at least one parameter of a synthetic image rendering apparatus for rendering synthetic images of objects of the specified type. The parameter controls an attribute of synthetic images of objects rendered by the rendering apparatus. The method computes an embedding factor of the received value using a second encoder. The factorized embedding is modified with the computed embedding factor. The method computes, using a decoder with the modified embedding as input, an output image of an object which is substantially the same as the real image except for the attribute controlled by the parameter.