  Facial motion retargeting is an important problem in both computer graphics
and vision, which involves capturing the performance of a human face and
transferring it to another 3D character. Learning 3D morphable model (3DMM)
parameters from 2D face images using convolutional neural networks is common in
2D face alignment, 3D face reconstruction etc. However, existing methods either
require an additional face detection step before retargeting or use a cascade
of separate networks to perform detection followed by retargeting in a
sequence. In this paper, we present a single end-to-end network to jointly
predict the bounding box locations and 3DMM parameters for multiple faces.
First, we design a novel multitask learning framework that learns a
disentangled representation of 3DMM parameters for a single face. Then, we
leverage the trained single face model to generate ground truth 3DMM parameters
for multiple faces to train another network that performs joint face detection
and motion retargeting for images with multiple faces. Experimental results
show that our joint detection and retargeting network has high face detection
accuracy and is robust to extreme expressions and poses while being faster than
state-of-the-art methods.
