DualSDF: Semantic shape manipulation using a two-level representation
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DualSDF : Semantic shape manipulation using a two-level representation. / Hao, Zekun; Averbuch-Elor, Hadar; Snavely, Noah; Belongie, Serge.
I: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, s. 7628-7638.Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
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TY - GEN
T1 - DualSDF
T2 - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020
AU - Hao, Zekun
AU - Averbuch-Elor, Hadar
AU - Snavely, Noah
AU - Belongie, Serge
N1 - Publisher Copyright: © 2020 IEEE.
PY - 2020
Y1 - 2020
N2 - We are seeing a Cambrian explosion of 3D shape representations for use in machine learning. Some representations seek high expressive power in capturing high-resolution detail. Other approaches seek to represent shapes as compositions of simple parts, which are intuitive for people to understand and easy to edit and manipulate. However, it is difficult to achieve both fidelity and interpretability in the same representation. We propose DualSDF, a representation expressing shapes at two levels of granularity, one capturing fine details and the other representing an abstracted proxy shape using simple and semantically consistent shape primitives. To achieve a tight coupling between the two representations, we use a variational objective over a shared latent space. Our two-level model gives rise to a new shape manipulation technique in which a user can interactively manipulate the coarse proxy shape and see the changes instantly mirrored in the high-resolution shape. Moreover, our model actively augments and guides the manipulation towards producing semantically meaningful shapes, making complex manipulations possible with minimal user input.
AB - We are seeing a Cambrian explosion of 3D shape representations for use in machine learning. Some representations seek high expressive power in capturing high-resolution detail. Other approaches seek to represent shapes as compositions of simple parts, which are intuitive for people to understand and easy to edit and manipulate. However, it is difficult to achieve both fidelity and interpretability in the same representation. We propose DualSDF, a representation expressing shapes at two levels of granularity, one capturing fine details and the other representing an abstracted proxy shape using simple and semantically consistent shape primitives. To achieve a tight coupling between the two representations, we use a variational objective over a shared latent space. Our two-level model gives rise to a new shape manipulation technique in which a user can interactively manipulate the coarse proxy shape and see the changes instantly mirrored in the high-resolution shape. Moreover, our model actively augments and guides the manipulation towards producing semantically meaningful shapes, making complex manipulations possible with minimal user input.
UR - http://www.scopus.com/inward/record.url?scp=85094853868&partnerID=8YFLogxK
U2 - 10.1109/CVPR42600.2020.00765
DO - 10.1109/CVPR42600.2020.00765
M3 - Conference article
AN - SCOPUS:85094853868
SP - 7628
EP - 7638
JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
SN - 1063-6919
M1 - 9157166
Y2 - 14 June 2020 through 19 June 2020
ER -
ID: 301820330