Face Editing Using Part‐Based Optimization of the Latent Space
We propose an approach for interactive 3D face editing based on deep generative models. Most of the current face modeling methods rely on linear methods and cannot express complex and non‐linear deformations. In contrast to 3D morphable face models based on Principal Component Analysis (PCA), we int...
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| Published in: | Computer graphics forum Vol. 42; no. 2; pp. 269 - 279 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Oxford
Blackwell Publishing Ltd
01.05.2023
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | We propose an approach for interactive 3D face editing based on deep generative models. Most of the current face modeling methods rely on linear methods and cannot express complex and non‐linear deformations. In contrast to 3D morphable face models based on Principal Component Analysis (PCA), we introduce a novel architecture based on variational autoencoders. Our architecture has multiple encoders (one for each part of the face, such as the nose and mouth) which feed a single decoder. As a result, each sub‐vector of the latent vector represents one part. We train our model with a novel loss function that further disentangles the space based on different parts of the face. The output of the network is a whole 3D face. Hence, unlike part‐based PCA methods, our model learns to merge the parts intrinsically and does not require an additional merging process. To achieve interactive face modeling, we optimize for the latent variables given vertex positional constraints provided by a user. To avoid unwanted global changes elsewhere on the face, we only optimize the subset of the latent vector that corresponds to the part of the face being modified. Our editing optimization converges in less than a second. Our results show that the proposed approach supports a broader range of editing constraints and generates more realistic 3D faces. |
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| AbstractList | We propose an approach for interactive 3D face editing based on deep generative models. Most of the current face modeling methods rely on linear methods and cannot express complex and non‐linear deformations. In contrast to 3D morphable face models based on Principal Component Analysis (PCA), we introduce a novel architecture based on variational autoencoders. Our architecture has multiple encoders (one for each part of the face, such as the nose and mouth) which feed a single decoder. As a result, each sub‐vector of the latent vector represents one part. We train our model with a novel loss function that further disentangles the space based on different parts of the face. The output of the network is a whole 3D face. Hence, unlike part‐based PCA methods, our model learns to merge the parts intrinsically and does not require an additional merging process. To achieve interactive face modeling, we optimize for the latent variables given vertex positional constraints provided by a user. To avoid unwanted global changes elsewhere on the face, we only optimize the subset of the latent vector that corresponds to the part of the face being modified. Our editing optimization converges in less than a second. Our results show that the proposed approach supports a broader range of editing constraints and generates more realistic 3D faces. |
| Author | Popa, Tiberiu Beauchamp, Andre Aliari, Mohammad Amin Paquette, Eric |
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| Cites_doi | 10.1007/s11263-017-1009-7 10.1109/CVPR42600.2020.00347 10.1109/CVPR52688.2022.01969 10.1145/3395208 10.1002/cav.2028 10.1109/CVPR.2016.598 10.1109/CVPR.2019.01119 10.1109/TVCG.2013.249 10.1145/3130800.3130813 10.1109/CVPRW50498.2020.00142 10.1109/CVPR.2018.00612 10.1109/ICCV.2019.00731 10.1109/CVPR42600.2020.00520 10.1109/CVPR42600.2020.00068 10.1145/3528233.3530748 10.1109/CVPR.2018.00408 10.1007/978-3-030-58523-5_23 10.1145/3472954 10.1111/cgf.13830 10.1109/ICME.2019.00269 |
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| Copyright | 2023 Ubisoft Divertissements and The Authors. Computer Graphics Forum published by Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd. 2023. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | CCS Concepts Coders Computing methodologies → Mesh models Editing Modelling Neural networks Optimization Principal components analysis Three dimensional models |
| Title | Face Editing Using Part‐Based Optimization of the Latent Space |
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