A Data-Driven Creativity Measure for 3D Shapes

Manfred Lau; Luther Power

doi:10.1007/978-3-030-64556-4_47

A Data-Driven Creativity Measure for 3D Shapes

Manfred Lau^*
, Luther Power

^*Corresponding author for this work

School of Creative Media

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

There has been much interest in generating 3D shapes that are perceived to be “creative” and previous works develop tools that can be used to create shapes that may be considered “creative”. However, previous research either do not formally define what is a creative shape, or describe manually pre-defined methods or formulas to evaluate whether a shape is creative. In this paper, we develop a computational measure of 3D shape creativity by learning with raw data and without any pre-defined conception of creativity. We first collect various types of data on the human perception of 3D shape creativity. We then analyze the data to gain insights on what makes a shape creative, show results of our learned measure, and discuss some applications.

Original language	English
Title of host publication	Advances in Visual Computing
Subtitle of host publication	15th International Symposium, ISVC 2020 - Proceedings
Editors	George Bebis, Zhaozheng Yin, Edward Kim, Jan Bender, Kartic Subr, Bum Chul Kwon, Jian Zhao, Denis Kalkofen, George Baciu
Publisher	Springer, Cham
Pages	606-614
Number of pages	9
Volume	Part 1
ISBN (Electronic)	978-3-030-64556-4
ISBN (Print)	978-3-030-64555-7
DOIs	https://doi.org/10.1007/978-3-030-64556-4_47
Publication status	Published - 6 Oct 2020
Event	15th International Symposium on Visual Computing (ISVC) 2020 - Virtual conference, San Diego, United States Duration: 5 Oct 2020 → 7 Oct 2020 https://www.isvc.net/

Publication series

Name	Lecture Notes in Computer Science
Volume	12509
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Symposium on Visual Computing (ISVC) 2020
Abbreviated title	ISVC 2020
Place	United States
City	San Diego
Period	5/10/20 → 7/10/20
Internet address	https://www.isvc.net/

Bibliographical note

Information for this record is supplemented by the author(s) concerned.

Research Keywords

Mesh models
perception
creativity

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10.1007/978-3-030-64556-4_47

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Lau, M., & Power, L. (2020). A Data-Driven Creativity Measure for 3D Shapes. In G. Bebis, Z. Yin, E. Kim, J. Bender, K. Subr, B. C. Kwon, J. Zhao, D. Kalkofen, & G. Baciu (Eds.), Advances in Visual Computing: 15th International Symposium, ISVC 2020 - Proceedings (Vol. Part 1, pp. 606-614). (Lecture Notes in Computer Science; Vol. 12509). Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_47

Lau, Manfred ; Power, Luther. / A Data-Driven Creativity Measure for 3D Shapes. Advances in Visual Computing: 15th International Symposium, ISVC 2020 - Proceedings. editor / George Bebis ; Zhaozheng Yin ; Edward Kim ; Jan Bender ; Kartic Subr ; Bum Chul Kwon ; Jian Zhao ; Denis Kalkofen ; George Baciu. Vol. Part 1 Springer, Cham, 2020. pp. 606-614 (Lecture Notes in Computer Science).

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abstract = "There has been much interest in generating 3D shapes that are perceived to be “creative” and previous works develop tools that can be used to create shapes that may be considered “creative”. However, previous research either do not formally define what is a creative shape, or describe manually pre-defined methods or formulas to evaluate whether a shape is creative. In this paper, we develop a computational measure of 3D shape creativity by learning with raw data and without any pre-defined conception of creativity. We first collect various types of data on the human perception of 3D shape creativity. We then analyze the data to gain insights on what makes a shape creative, show results of our learned measure, and discuss some applications.",

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Lau, M & Power, L 2020, A Data-Driven Creativity Measure for 3D Shapes. in G Bebis, Z Yin, E Kim, J Bender, K Subr, BC Kwon, J Zhao, D Kalkofen & G Baciu (eds), Advances in Visual Computing: 15th International Symposium, ISVC 2020 - Proceedings. vol. Part 1, Lecture Notes in Computer Science, vol. 12509, Springer, Cham, pp. 606-614, 15th International Symposium on Visual Computing (ISVC) 2020, San Diego, United States, 5/10/20. https://doi.org/10.1007/978-3-030-64556-4_47

A Data-Driven Creativity Measure for 3D Shapes. / Lau, Manfred; Power, Luther.
Advances in Visual Computing: 15th International Symposium, ISVC 2020 - Proceedings. ed. / George Bebis; Zhaozheng Yin; Edward Kim; Jan Bender; Kartic Subr; Bum Chul Kwon; Jian Zhao; Denis Kalkofen; George Baciu. Vol. Part 1 Springer, Cham, 2020. p. 606-614 (Lecture Notes in Computer Science; Vol. 12509).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A Data-Driven Creativity Measure for 3D Shapes

AU - Lau, Manfred

AU - Power, Luther

N1 - Information for this record is supplemented by the author(s) concerned.

PY - 2020/10/6

Y1 - 2020/10/6

N2 - There has been much interest in generating 3D shapes that are perceived to be “creative” and previous works develop tools that can be used to create shapes that may be considered “creative”. However, previous research either do not formally define what is a creative shape, or describe manually pre-defined methods or formulas to evaluate whether a shape is creative. In this paper, we develop a computational measure of 3D shape creativity by learning with raw data and without any pre-defined conception of creativity. We first collect various types of data on the human perception of 3D shape creativity. We then analyze the data to gain insights on what makes a shape creative, show results of our learned measure, and discuss some applications.

AB - There has been much interest in generating 3D shapes that are perceived to be “creative” and previous works develop tools that can be used to create shapes that may be considered “creative”. However, previous research either do not formally define what is a creative shape, or describe manually pre-defined methods or formulas to evaluate whether a shape is creative. In this paper, we develop a computational measure of 3D shape creativity by learning with raw data and without any pre-defined conception of creativity. We first collect various types of data on the human perception of 3D shape creativity. We then analyze the data to gain insights on what makes a shape creative, show results of our learned measure, and discuss some applications.

KW - Mesh models

KW - perception

KW - creativity

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DO - 10.1007/978-3-030-64556-4_47

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-030-64555-7

VL - Part 1

T3 - Lecture Notes in Computer Science

SP - 606

EP - 614

BT - Advances in Visual Computing

A2 - Bebis, George

A2 - Yin, Zhaozheng

A2 - Kim, Edward

A2 - Bender, Jan

A2 - Subr, Kartic

A2 - Kwon, Bum Chul

A2 - Zhao, Jian

A2 - Kalkofen, Denis

A2 - Baciu, George

PB - Springer, Cham

T2 - 15th International Symposium on Visual Computing (ISVC) 2020

Y2 - 5 October 2020 through 7 October 2020

ER -

A Data-Driven Creativity Measure for 3D Shapes

Abstract

Publication series

Conference

Bibliographical note

Research Keywords

Access Output

Other Access Options

Permanent Link

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GRF: Task-aware Visual Mesh Saliency

Cite this

A Data-Driven Creativity Measure for 3D Shapes

Abstract

Publication series

Conference

Bibliographical note

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

GRF: Task-aware Visual Mesh Saliency

Cite this