Download | - View final version: Robust body shape correspondence with anthropometric landmarks (PDF, 1.4 MiB)
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DOI | Resolve DOI: https://doi.org/10.15221/22.17 |
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Author | Search for: Jiao, Yibo; Search for: Shu, Chang1; Search for: Pai, Dinesh K. |
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Affiliation | - National Research Council of Canada. Digital Technologies
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Format | Text, Article |
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Conference | 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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Physical description | 7 p. |
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Subject | shape matching; deep learning; anthropometry |
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Abstract | 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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Publication date | 2022-10-25 |
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Publisher | Hometrica Consulting (Dr. Nicola D'Apuzzo) |
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Other format | |
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Language | English |
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Peer reviewed | Yes |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | 3acbd2da-594d-4a55-adb9-135c37491e24 |
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Record created | 2023-01-30 |
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Record modified | 2023-01-30 |
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