Learning Detail Transfer based on Geometric Features

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Supplemental Material for:

Learning Detail Transfer based on Geometric Features

Sema Berkiten

¹

Maciej Halber

¹

Justin Solomon

²

Chongyang Ma

³

Hao Li

^3,4,5

Szymon Rusinkiewicz

¹

1

Princeton University

²

MIT

³

University of Southern California

⁴

USC Institute for Creative Technologies

⁵

Pinscreen

In this work, we use λ ≡ 0.1 for metric learning and three different patch sizes, n, (7x7, 9x9, and 11x11) for the texture synthesis. We observe that different patch sizes give rise to different but viable synthesis results in which the bigger the patch size is the more the local coherency is preserved. This supplemental material includes results for shapes in clothing and furniture classes.

Figure 1: Source models.

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2017 The Author(s)

Computer Graphics Forum c2017 The Eurographics Association and John Wiley & Sons Ltd.

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EUROGRAPHICS 2017 / L. Barthe and B. Benes (Guest Editors)

Volume 36 (2017), Number 2

Figure 2: Detail transfer for furniture models. For each source mesh (left column), we transfer details to three different target meshes.

Figure 3: Detail transfer on faces from Merl Database from the source models in blue to the target models in pink.

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2017 The Author(s)

Computer Graphics Forum c2017 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

(a) Source Front (b) Target Front (c) Result Front

(d) Source Back (e) Target Back (f) Result Back

Figure 4: Detail transfer from a crocodile head to a snake head.

(a) Source Front (b) Target Front (c) Result Front

(d) Source Back (e) Target Back (f) Result Back

Figure 5: Detail transfer from a crocodile head to a cushion. For this semantically unrelated source-target pair, the detail transfer is mostly guided by the surface orientation.

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2017 The Author(s)

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Clothing. We generated a significant number of results using 6 high-quality source models and 11 low-polygonal target models. Our algorithm generates multiple results for each source-target pair for different patch sizes in the synthesis part of the algorithm.

Figure 6: Source models.

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2017 The Author(s) Computer Graphics Forum c2017 The Eurographics Association and John Wiley & Sons Ltd.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 7: Results.

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2017 The Author(s)

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 8: Results.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 9: Results.

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2017 The Author(s)

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 10: Results.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 11: Results.

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2017 The Author(s)

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 12: Results.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 13: Results.

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2017 The Author(s)

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 14: Results.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 15: Results.

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2017 The Author(s)

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 16: Results.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Furniture. We show results generated from 12 high-quality source models and 17 low-polygonal target models below. Our algorithm generates multiple results for each source-target pair for different patch sizes in the synthesis part of the algorithm.

Figure 17: Source models.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 18: Results.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 19: Results.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 20: Results.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 21: Results.

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Berkiten et al. / Supplemental Material for: Learning Detail Transfer based on Geometric Features

Figure 22: Results.

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