Shape My Face: Registering 3D Face Scans by Surface-to-Surface
Translation
- URL: http://arxiv.org/abs/2012.09235v2
- Date: Wed, 10 Mar 2021 15:25:41 GMT
- Title: Shape My Face: Registering 3D Face Scans by Surface-to-Surface
Translation
- Authors: Mehdi Bahri, Eimear O' Sullivan, Shunwang Gong, Feng Liu, Xiaoming
Liu, Michael M. Bronstein, Stefanos Zafeiriou
- Abstract summary: Shape-My-Face (SMF) is a powerful encoder-decoder architecture based on an improved point cloud encoder, a novel visual attention mechanism, graph convolutional decoders with skip connections, and a specialized mouth model.
Our model provides topologically-sound meshes with minimal supervision, offers faster training time, has orders of magnitude fewer trainable parameters, is more robust to noise, and can generalize to previously unseen datasets.
- Score: 75.59415852802958
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Standard registration algorithms need to be independently applied to each
surface to register, following careful pre-processing and hand-tuning.
Recently, learning-based approaches have emerged that reduce the registration
of new scans to running inference with a previously-trained model. In this
paper, we cast the registration task as a surface-to-surface translation
problem, and design a model to reliably capture the latent geometric
information directly from raw 3D face scans. We introduce Shape-My-Face (SMF),
a powerful encoder-decoder architecture based on an improved point cloud
encoder, a novel visual attention mechanism, graph convolutional decoders with
skip connections, and a specialized mouth model that we smoothly integrate with
the mesh convolutions. Compared to the previous state-of-the-art learning
algorithms for non-rigid registration of face scans, SMF only requires the raw
data to be rigidly aligned (with scaling) with a pre-defined face template.
Additionally, our model provides topologically-sound meshes with minimal
supervision, offers faster training time, has orders of magnitude fewer
trainable parameters, is more robust to noise, and can generalize to previously
unseen datasets. We extensively evaluate the quality of our registrations on
diverse data. We demonstrate the robustness and generalizability of our model
with in-the-wild face scans across different modalities, sensor types, and
resolutions. Finally, we show that, by learning to register scans, SMF produces
a hybrid linear and non-linear morphable model. Manipulation of the latent
space of SMF allows for shape generation, and morphing applications such as
expression transfer in-the-wild. We train SMF on a dataset of human faces
comprising 9 large-scale databases on commodity hardware.
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