Deep Appearance Avatar Rendering for Real-Time Facial Detail
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Solution Overview
Problem
Existing computer graphics models struggle with real-time rendering of human faces in virtual and augmented reality, particularly in capturing fine geometric structures like eyelashes and vellus hair, which hinders photorealistic and dynamic rendering of social interactions.
Innovation Solution
A deep appearance model using an autoencoder architecture jointly encodes geometry and view-dependent textures, enabling real-time rendering of avatars by learning a joint representation of facial geometry and texture, and predicting view-dependent effects without explicitly modeling fine features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional computer graphics models are used to model fine geometric structures like eyelashes and vellus hair, then photorealistic rendering may be achieved, but the device complexity and computational burden increase significantly, making real-time rendering difficult
Solution Approach 1:
The patent extracts fine geometric structures like eyelashes and vellus hair from explicit 3D modeling and represents them implicitly through 2D texture maps. The texture maps capture appearance details without requiring complex geometric models, thereby reducing device complexity while maintaining rendering precision.
Solution Approach 2:
The patent uses 2D texture maps as copies of fine geometric structures rather than modeling them with explicit 3D geometry. This copying approach allows photorealistic rendering of details like pores and hair without the computational burden of full 3D modeling, resolving the contradiction between rendering precision and model complexity.
2Manufacturing precision
If detailed geometric structures are explicitly modeled to achieve photorealistic rendering, then rendering quality improves, but the processing time increases and real-time rendering becomes difficult
Solution Approach 1:
The patent replaces the mechanical process of explicit 3D geometric modeling with a data-driven approach using neural networks. The network learns to generate photorealistic textures from input images, substituting complex geometric computations with efficient texture synthesis, thereby reducing processing time while maintaining rendering quality.
Solution Approach 2:
The patent performs preliminary encoding of appearance information into texture maps during an offline training phase. The neural network learns to generate high-quality textures in advance, so that during real-time rendering, pre-learned patterns can be quickly applied without requiring complex on-the-fly geometric computations, thus reducing processing time.
3Manufacturing precision
If view-dependent textures are used to capture lighting and reflection effects, then rendering realism improves, but the amount of data and computational resources required increase
Solution Approach 1:
The patent changes the parameter representation of textures from storing multiple view-dependent texture maps to using a single texture map combined with view-dependent rendering equations. By parameterizing the view-dependent effects mathematically rather than storing them as additional data, the patent reduces data quantity while maintaining texture realism.
Data Source
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AI summary
The disclosed computer system may include an input module, an autoencoder, and a rendering module. The input module may receive geometry information and images of a subject. The geometry information may be indicative of variation in geometry of the subject over time. Each image may be associated with a respective viewpoint and may include a view-dependent texture map of the subject. The autoencoder may jointly encode texture information and the geometry information to provide a latent vector. The autoencoder may infer, using the latent vector, an inferred geometry and an inferred view-dependent texture of the subject for a predicted viewpoint. The rendering module may be configured to render a reconstructed image of the subject for the predicted viewpoint using the inferred geometry and the inferred view-dependent texture. Various other systems and methods are also disclosed.