**Detailed Expression Capture and Animation**, or **DECA**, is a model for 3D face reconstruction that is trained to robustly produce a UV displacement map from a low-dimensional latent representation that consists of person-specific detail parameters and generic expression parameters, while a regressor is trained to predict detail, shape, albedo, expression, pose and illumination parameters from a single image. A detail-consistency loss is used to disentangle person-specific details and expression-dependent wrinkles. This disentanglement allows us to synthesize realistic person-specific wrinkles by controlling expression parameters while keeping person-specific details unchanged.

NN4G is based on a constructive feedforward architecture with state variables that uses neurons with no feedback connections. The neurons are applied to the input graphs by a general traversal process that relaxes the constraints of previous approaches derived by the causality assumption over hierarchical input data.

Description from: [Neural network for graphs: a contextual constructive approach](https://www.meta.org/papers/neural-network-for-graphs-a-contextual/19193509)

NN4G

DECA

Learning an Animatable Detailed 3D Face Model from In-The-Wild Images

A neural network model to automatically capture trends in time-series data for improved prediction/forecasting performance

Source	Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com