Unsupervised Depth Estimation, 3D Face Rotation and Replacement
arXiv:1803.09202
Abstract
We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also predicting 3D viewpoint transformations that match a desired pose and facial geometry. We achieve this by inferring the depth of facial keypoints of an input image in an unsupervised manner, without using any form of ground-truth depth information. We show how it is possible to use these depths as intermediate computations within a new backpropable loss to predict the parameters of a 3D affine transformation matrix that maps inferred 3D keypoints of an input face to the corresponding 2D keypoints on a desired target facial geometry or pose. Our resulting approach, called DepthNets, can therefore be used to infer plausible 3D transformations from one face pose to another, allowing faces to be frontalized, transformed into 3D models or even warped to another pose and facial geometry. Lastly, we identify certain shortcomings with our formulation, and explore adversarial image translation techniques as a post-processing step to re-synthesize complete head shots for faces re-targeted to different poses or identities.
arXiv commentDepth Estimation, Face Rotation, Face Swap, 32nd Conference on Neural Information Processing Systems (NIPS 2018)
References in corpus (7)
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
- Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
- Unsupervised Learning of Depth and Ego-Motion from Video
- Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
- Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
- Towards Large-Pose Face Frontalization in the Wild