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Found 6 papers, 4 papers with code
3D Morphable Models as Spatial Transformer Networks
In this paper, we show how a 3D Morphable Model (i. e. a statistical model of the 3D shape of a class of objects such as faces) can be used to spatially transform input data as a module (a 3DMM-STN) within a convolutional neural network.
Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences
We propose a fully automatic method for fitting a 3D morphable model to single face images in arbitrary pose and lighting.
What does 2D geometric information really tell us about 3D face shape?
We show that this is not the case and that geometric information is an ambiguous cue.
Statistical transformer networks: learning shape and appearance models via self supervision
In this configuration, our model learns an active appearance model and a means to fit the model from scratch with no supervision at all, even identity labels.
Exploring Transformers in Natural Language Generation: GPT, BERT, and XLNet
Recent years have seen a proliferation of attention mechanisms and the rise of Transformers in Natural Language Generation (NLG).
AfriKI: Machine-in-the-Loop Afrikaans Poetry Generation
This paper proposes a generative language model called AfriKI.
