Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation
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 potent...
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| Vydané v: | International journal of computer vision Ročník 129; číslo 9; s. 2680 - 2713 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.09.2021
Springer Springer Nature B.V |
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| ISSN: | 0920-5691, 1573-1405 |
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| 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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| AbstractList | 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. |
| Audience | Academic |
| Author | Liu, Xiaoming O’ Sullivan, Eimear Bahri, Mehdi Gong, Shunwang Liu, Feng Zafeiriou, Stefanos Bronstein, Michael M. |
| Author_xml | – sequence: 1 givenname: Mehdi orcidid: 0000-0002-2409-0261 surname: Bahri fullname: Bahri, Mehdi email: m.bahri@imperial.ac.uk organization: Department of Computing, Imperial College London – sequence: 2 givenname: Eimear orcidid: 0000-0003-0525-3341 surname: O’ Sullivan fullname: O’ Sullivan, Eimear organization: Department of Computing, Imperial College London – sequence: 3 givenname: Shunwang orcidid: 0000-0001-8717-8722 surname: Gong fullname: Gong, Shunwang organization: Department of Computing, Imperial College London – sequence: 4 givenname: Feng orcidid: 0000-0003-2103-4659 surname: Liu fullname: Liu, Feng organization: Department of Computer Science and Engineering, Michigan State University – sequence: 5 givenname: Xiaoming orcidid: 0000-0003-3215-8753 surname: Liu fullname: Liu, Xiaoming organization: Department of Computer Science and Engineering, Michigan State University – sequence: 6 givenname: Michael M. orcidid: 0000-0002-1262-7252 surname: Bronstein fullname: Bronstein, Michael M. organization: Department of Computing, Imperial College London – sequence: 7 givenname: Stefanos orcidid: 0000-0002-5222-1740 surname: Zafeiriou fullname: Zafeiriou, Stefanos organization: Department of Computing, Imperial College London |
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| Title | Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation |
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