arXiv:1503.03167cs.CV
Deep Convolutional Inverse Graphics Network
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that learns an interpretable representation of images. This representation is disentangled with respect to transformations such as out-of-plane rotations and lighting variations. The DC-IGN model is composed of multiple layers of convolution and de-convolution operators and is trained using the Stochastic Gradient Variational Bayes (SGVB) algorithm. We propose a training procedure to encourage neurons in the graphics code layer to represent a specific transformation (e.g. pose or light). Given a single input image, our model can generate new images of the same object with variations in pose and lighting. We present qualitative and quantitative results of the model's efficacy at learning a 3D rendering engine.
I'm surprised people are so blown away by this. It's a cool demonstration, but for this problem you have basically infinite training data. If you can find a latent space of faces this is hardly a stretch, since you already have a fantastic notion of locality in your data (by…
TTProgramsView on HNParent context