GANimation: Anatomically-aware Facial Animation from a Single Image

Task

Continuous changing of AUs using unsupervised GAN.

Formally, it learns a model

Given

Related Works

DIAT, CycleGAN, IcGAN, StarGAN

Method

Losses (four terms)

• traditional GAN loss: try to make the generated image as real as possible

• Attention loss: keep the mask smooth (TV) and force the mask to use the generated color map (L2)

• Conditional Expression Loss: training a separate AU regressor

• Identity Loss: nothing but a cycle loss

Results

Dataset is EmotioNet dataset, result is amazing. The model could generate continuous AU changes, which is what I currently working on, SAD…

To tell the truth, this paper does not introduce really new stuff, WGAN-GP, TV loss, extra classifier/regressor, identity (cycle) loss, these are all techniques that have been used before, the good part is they combine them into a one single model and get outstanding performance! Excellent job!

Reference

[1] http://www.albertpumarola.com/research/GANimation/index.html