BLiSS: Bootstrapped Linear Shape Space
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866911774018633728 |
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| author | Muralikrishnan, Sanjeev Huang, Chun-Hao Paul Ceylan, Duygu Mitra, Niloy J. |
| author_facet | Muralikrishnan, Sanjeev Huang, Chun-Hao Paul Ceylan, Duygu Mitra, Niloy J. |
| contents | Morphable models are fundamental to numerous human-centered processes as they offer a simple yet expressive shape space. Creating such morphable models, however, is both tedious and expensive. The main challenge is establishing dense correspondences across raw scans that capture sufficient shape variation. This is often addressed using a mix of significant manual intervention and non-rigid registration. We observe that creating a shape space and solving for dense correspondence are tightly coupled -- while dense correspondence is needed to build shape spaces, an expressive shape space provides a reduced dimensional space to regularize the search. We introduce BLiSS, a method to solve both progressively. Starting from a small set of manually registered scans to bootstrap the process, we enrich the shape space and then use that to get new unregistered scans into correspondence automatically. The critical component of BLiSS is a non-linear deformation model that captures details missed by the low-dimensional shape space, thus allowing progressive enrichment of the space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_01765 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | BLiSS: Bootstrapped Linear Shape Space Muralikrishnan, Sanjeev Huang, Chun-Hao Paul Ceylan, Duygu Mitra, Niloy J. Computer Vision and Pattern Recognition Morphable models are fundamental to numerous human-centered processes as they offer a simple yet expressive shape space. Creating such morphable models, however, is both tedious and expensive. The main challenge is establishing dense correspondences across raw scans that capture sufficient shape variation. This is often addressed using a mix of significant manual intervention and non-rigid registration. We observe that creating a shape space and solving for dense correspondence are tightly coupled -- while dense correspondence is needed to build shape spaces, an expressive shape space provides a reduced dimensional space to regularize the search. We introduce BLiSS, a method to solve both progressively. Starting from a small set of manually registered scans to bootstrap the process, we enrich the shape space and then use that to get new unregistered scans into correspondence automatically. The critical component of BLiSS is a non-linear deformation model that captures details missed by the low-dimensional shape space, thus allowing progressive enrichment of the space. |
| title | BLiSS: Bootstrapped Linear Shape Space |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.01765 |