5 research outputs found
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Learning a Generative Model of Human Faces Through Inverse Rendering
Generative models in an inverse graphics framework are appealing models for visual perception. How might childrenacquire them? We present a computational procedure for learning generative models of human faces using developmen-tally plausible input. Our statistical model of shape and appearance initially uses the average face as a template with asimple Gaussian process model of deformations. We iteratively learn the statistical distribution of faces by performinganalysis-by-synthesis on a small number of images and combine the results to construct an improved generative model.Our analysis-by-synthesis framework combines a convolutional neural network for fast inference with a Markov chainMonte Carlo process for detailed refinement. This learning strategy quickly captures the variation of natural faces anddemonstrates an efficient way to learn the distribution of faces
Building 3D Generative Models from Minimal Data
Abstract
We propose a method for constructing generative models of 3D objects from a single 3D mesh and improving them through unsupervised low-shot learning from 2D images. Our method produces a 3D morphable model that represents shape and albedo in terms of Gaussian processes. Whereas previous approaches have typically built 3D morphable models from multiple high-quality 3D scans through principal component analysis, we build 3D morphable models from a single scan or template. As we demonstrate in the face domain, these models can be used to infer 3D reconstructions from 2D data (inverse graphics) or 3D data (registration). Specifically, we show that our approach can be used to perform face recognition using only a single 3D template (one scan total, not one per person). We extend our model to a preliminary unsupervised learning framework that enables the learning of the distribution of 3D faces using one 3D template and a small number of 2D images. Our approach is motivated as a potential model for the origins of face perception in human infants, who appear to start with an innate face template and subsequently develop a flexible system for perceiving the 3D structure of any novel face from experience with only 2D images of a relatively small number of familiar faces