| Téléchargement | - Voir la version finale : Robust body shape correspondence with anthropometric landmarks (PDF, 1.4 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.15221/22.17 |
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| Auteur | Rechercher : Jiao, Yibo; Rechercher : Shu, Chang1; Rechercher : Pai, Dinesh K. |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 3DBODY.TECH 2022 - 13th International Conference and Exhibition on 3D Body Scanning and Processing Technologies, Oct. 25-26, 2022, Lugano, Switzerland |
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| Description physique | 7 p. |
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| Sujet | shape matching; deep learning; anthropometry |
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| Résumé | We propose a method to improve the robustness of state-of-art learning-based methods for finding point-to-point correspondences of 3D human models with anthropometric landmarks. Specifically, current deep learning-based methods generally focus on intrinsic, local, properties of body shapes, which lack extrinsic global information. Thus, these methods are challenged by matching ambiguities, for instance, due to the bilateral symmetry of human body shapes. We demonstrate our method with an unsupervised learning-based method, DeepShells. Our work introduces a landmark supervision method based on the Shells by adding linear soft constraints to minimize this problem that we term the "intrinsic feature ambiguity problem." To that end, we derive a simple but efficient pipeline that better distinguishes self-similarities yet has similar overall matching quality. |
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| Date de publication | 2022-10-25 |
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| Maison d’édition | Hometrica Consulting (Dr. Nicola D'Apuzzo) |
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| Dans | |
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| Autre format | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 3acbd2da-594d-4a55-adb9-135c37491e24 |
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| Enregistrement créé | 2023-01-30 |
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| Enregistrement modifié | 2023-01-30 |
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