Robust semantic analysis by synthesis of 3D facial motion

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Generating 3D faces using Convolutional Mesh Autoencoders (2018, methodology): “Our main contributions are: 1) we introduce a Convolutional Mesh Autoencoder consisting of mesh downsampling and mesh upsampling layers with fast localized convolutional filters defined on the mesh surface; 2) we show that our model accurately represents 3D faces in a low-dimensional latent space pe”
Random Forests for Real Time 3D Face Analysis (2012, background): “Yet, 3D imagery has mainly been leveraged for face tracking (Breidt et al. 2011; Cai et al. 2010; Weise et al. 2009a, 2011), often leaving open issues of drift and (re-)initialization.”
A Survey on Human Motion Analysis from Depth Data (2013, methodology): “The approach [108] uses a time-of-flight camera to estimate a few basic facial action units based on the Facial Action Coding System (FACS).”
3D Constrained Local Model for rigid and non-rigid facial tracking (2012, methodology): “Some notable exceptions are attempts of deformable model fitting on depth images directly through the use of iterative closest point like algorithms [3, 5].”