Neural Face Rendering With Controllable Attribute Embeddings
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Solution Overview
Problem
Existing image rendering techniques struggle to generate realistic human faces with controllable attributes without detailed hand-labeling of datasets, failing to generalize to out-of-distribution combinations, and require time-consuming 3D asset creation and animation.
Innovation Solution
A neural renderer using two encoders and a decoder, trained on both real and synthetic images, allows for semantically meaningful control over attributes like facial hair and expression, enabling efficient generation of photorealistic images without detailed hand-labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional rendering pipelines are used to generate photorealistic human faces, then manufacturing precision of 3D assets is improved, but device complexity and time consumption increase significantly
Solution Approach 1:
The patent replaces the mechanical 3D asset creation pipeline with a neural network-based system. Instead of using traditional rendering engines that require 3D models, rigged assets, and manual animation, the system uses a trained neural network that generates photorealistic face images directly from text prompts or reference images, eliminating the need for complex 3D modeling software and manual asset creation
Solution Approach 2:
The patent creates synthetic training data by copying and transforming existing face images through various augmentations (rotation, scaling, color jittering, etc.). This synthetic data is then used to train the neural network, allowing the system to learn from numerous examples without requiring manual 3D asset creation for each scenario
2Adaptability or versatility
If conditional GANs are trained with detailed hand labels to enable control over generation, then adaptability of attribute control is improved, but measurement precision and time consumption increase
Solution Approach 1:
The patent performs preliminary actions by pre-training the neural network on a large dataset of diverse face images with various attributes (different ethnicities, ages, genders, expressions, lighting conditions). This pre-training enables the model to learn the relationships between visual features and their corresponding attributes, allowing for controlled generation without requiring detailed hand-labeling of new datasets
Solution Approach 2:
The patent applies various data augmentation techniques that create partial modifications of original images (rotation, scaling, cropping, color adjustments). This excessive action of creating many variations from limited data allows the model to learn robust attribute control without needing equally extensive labeled datasets
3Adaptability or versatility
If conditional models are trained to generalize to out-of-distribution combinations, then adaptability is improved, but reliability and manufacturing precision deteriorate
Solution Approach 1:
The patent changes the parameter space by training on diverse combinations of attributes (different races, ages, genders, expressions, lighting conditions) and using data augmentations that create out-of-distribution samples. This exposes the model to a wide range of parameter combinations during training, enabling it to generalize reliably to new, unseen attribute combinations while maintaining image quality
4Reliability
If 3D capture and rigged models are used to create animations, then realism of animation is improved, but productivity and time efficiency decrease
Solution Approach 1:
The patent replaces the mechanical process of 3D capture, rigging, and manual animation with a neural network that directly generates animated face images. The system takes a reference face image and text prompts describing desired expressions or actions, then generates the corresponding animated images in seconds, eliminating the time-consuming 3D pipeline while maintaining photorealism
Data Source
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.