Journal article
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation
International journal of computer vision, v 129(9), pp 2680-2713
01 Sep 2021
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. The potential benefits are multifold: inference is typically orders of magnitude faster than solving a new instance of a difficult optimization problem, deep learning models can be made robust to noise and corruption, and the trained model may be re-used for other tasks, e.g. through transfer learning. 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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Details
- Title
- Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation
- Creators
- Mehdi Bahri - Imperial College LondonEimear O' Sullivan - Imperial Coll London, Dept Comp, London, EnglandShunwang Gong - Imperial College LondonFeng Liu - Drexel University, Computer Science (Computing)Xiaoming Liu - Michigan State UniversityMichael M. Bronstein - Imperial College LondonStefanos Zafeiriou - Imperial College London
- Publication Details
- International journal of computer vision, v 129(9), pp 2680-2713
- Publisher
- Springer Nature
- Number of pages
- 34
- Grant note
- Department of Computing scholarship 724228 / ERC Consolidator grant; European Research Council (ERC) Qualcomm Innovation Fellowship EP/S010203/1 / EPSRC; UK Research & Innovation (UKRI); Engineering & Physical Sciences Research Council (EPSRC) EP/S010203/1 / EPSRC Fellowship DEFORM: Large Scale Shape Analysis of Deformable Models of Humans; UK Research & Innovation (UKRI); Engineering & Physical Sciences Research Council (EPSRC)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000671637200001
- Scopus ID
- 2-s2.0-85110313487
- Other Identifier
- 991021906502004721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence