3D Geometry Sequence Synthesis for Data-Efficient Face Animation
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Solution Overview
Problem
Existing machine learning models for generating digital faces require extensive and resource-intensive data collection in controlled settings, limiting their ability to generalize to new data and learn the relationship between input images and output meshes or animations due to the scarcity of training samples.
Innovation Solution
A technique involving an encoder neural network converts input geometries into latent vectors, generating a sequence of geometries based on these vectors, and using a decoder to produce realistic movement-based performances, leveraging synthetic data to train the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a deep neural network is trained to perform 3D reconstruction or animation of a face using conventional machine learning approaches, then the model can generate digital faces from images, but the training process requires a large amount of time and resources to collect and process real-world training data under controlled settings
Solution Approach 1:
The patent uses synthetic 3D face data generated by a 3D face model as training data for the deep neural network, replacing the need to collect real-world training data. The synthetic data includes rendered images and corresponding 3D mesh information, creating a virtual training dataset that eliminates time-consuming data collection while providing sufficient variety for generalization
Solution Approach 2:
The patent changes the parameter space by generating 3D face models with varied parameters including different identities, expressions, poses, and lighting conditions. This allows the training dataset to cover diverse scenarios without requiring actual physical data collection, maintaining generalization capability while reducing data collection overhead
2Manufacturing precision
If real-world training data is collected using facial capture systems in controlled settings, then accurate 3D geometry and appearance maps can be obtained, but the capture systems are limited to scanning a small number of people which restricts the diversity of training samples
Solution Approach 1:
The patent creates synthetic copies of diverse 3D face models with varying parameters (identity, expression, pose, lighting) rather than relying on physical scanning of limited subjects. This approach maintains geometric accuracy through precise 3D modeling while achieving unlimited diversity in the training dataset
Solution Approach 2:
The patent transitions from the physical dimension (actual face scanning) to the digital/synthetic dimension (computer-generated 3D models). This dimensional shift allows unlimited variation in training samples through parameter manipulation while maintaining accurate 3D geometry through controlled rendering
3Reliability
If a large number of training samples with diverse variations are used to train the deep neural network, then the model's ability to generalize improves, but collecting and processing such extensive training data becomes intractable
Solution Approach 1:
The patent uses synthetic data generation to create unlimited training samples without the complexity of physical data collection infrastructure. The 3D face model can generate diverse training pairs (images and 3D meshes) through parameter variation, eliminating the need for complex capture systems and large-scale data processing pipelines
Data Source
AI summary
A technique for generating a sequence of geometries includes converting, via an encoder neural network, one or more input geometries corresponding to one or more frames within an animation into one or more latent vectors. The technique also includes generating the sequence of geometries corresponding to a sequence of frames within the animation based on the one or more latent vectors. The technique further includes causing output related to the animation to be generated based on the sequence of geometries.