Analogy-driven 3D style transfer

Style transfer aims to apply the style of an exemplar model to a target one, while retaining the target's structure. The main challenge in this process is to algorithmically distinguish style from structure, a high‐level, potentially ill‐posed cognitive task. Inspired by cognitive science resea...

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Published in:Computer graphics forum Vol. 33; no. 2; pp. 175 - 184
Main Authors: Ma, Chongyang, Huang, Haibin, Sheffer, Alla, Kalogerakis, Evangelos, Wang, Rui
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.05.2014
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ISSN:0167-7055, 1467-8659
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Abstract Style transfer aims to apply the style of an exemplar model to a target one, while retaining the target's structure. The main challenge in this process is to algorithmically distinguish style from structure, a high‐level, potentially ill‐posed cognitive task. Inspired by cognitive science research we recast style transfer in terms of shape analogies. In IQ testing, shape analogy queries present the subject with three shapes: source, target and exemplar, and ask them to select an output such that the transformation, or analogy, from the exemplar to the output is similar to that from the source to the target. The logical process involved in identifying the source‐to‐target analogies implicitly detects the structural differences between the source and target and can be used effectively to facilitate style transfer. Since the exemplar has a similar structure to the source, applying the analogy to the exemplar will provide the output we seek. The main technical challenge we address is to compute the source to target analogies, consistent with human logic. We observe that the typical analogies we look for consist of a small set of simple transformations, which when applied to the exemplar generate a continuous, seamless output model. To assemble a shape analogy, we compute an optimal set of source‐to‐target transformations, such that the assembled analogy best fits these criteria. The assembled analogy is then applied to the exemplar shape to produce the desired output model. We use the proposed framework to seamlessly transfer a variety of style properties between 2D and 3D objects and demonstrate significant improvements over the state of the art in style transfer. We further show that our framework can be used to successfully complete partial scans with the help of a user provided structural template, coherently propagating scan style across the completed surfaces.
AbstractList Style transfer aims to apply the style of an exemplar model to a target one, while retaining the target's structure. The main challenge in this process is to algorithmically distinguish style from structure, a high‐level, potentially ill‐posed cognitive task. Inspired by cognitive science research we recast style transfer in terms of shape analogies . In IQ testing, shape analogy queries present the subject with three shapes: source, target and exemplar , and ask them to select an output such that the transformation, or analogy , from the exemplar to the output is similar to that from the source to the target. The logical process involved in identifying the source‐to‐target analogies implicitly detects the structural differences between the source and target and can be used effectively to facilitate style transfer. Since the exemplar has a similar structure to the source, applying the analogy to the exemplar will provide the output we seek. The main technical challenge we address is to compute the source to target analogies, consistent with human logic. We observe that the typical analogies we look for consist of a small set of simple transformations, which when applied to the exemplar generate a continuous, seamless output model. To assemble a shape analogy, we compute an optimal set of source‐to‐target transformations, such that the assembled analogy best fits these criteria. The assembled analogy is then applied to the exemplar shape to produce the desired output model. We use the proposed framework to seamlessly transfer a variety of style properties between 2D and 3D objects and demonstrate significant improvements over the state of the art in style transfer. We further show that our framework can be used to successfully complete partial scans with the help of a user provided structural template, coherently propagating scan style across the completed surfaces.
Style transfer aims to apply the style of an exemplar model to a target one, while retaining the target's structure. The main challenge in this process is to algorithmically distinguish style from structure, a high-level, potentially ill-posed cognitive task. Inspired by cognitive science research we recast style transfer in terms of shape analogies. In IQ testing, shape analogy queries present the subject with three shapes: source, target and exemplar, and ask them to select an output such that the transformation, or analogy, from the exemplar to the output is similar to that from the source to the target. The logical process involved in identifying the source-to-target analogies implicitly detects the structural differences between the source and target and can be used effectively to facilitate style transfer. Since the exemplar has a similar structure to the source, applying the analogy to the exemplar will provide the output we seek. The main technical challenge we address is to compute the source to target analogies, consistent with human logic. We observe that the typical analogies we look for consist of a small set of simple transformations, which when applied to the exemplar generate a continuous, seamless output model. To assemble a shape analogy, we compute an optimal set of source-to-target transformations, such that the assembled analogy best fits these criteria. The assembled analogy is then applied to the exemplar shape to produce the desired output model. We use the proposed framework to seamlessly transfer a variety of style properties between 2D and 3D objects and demonstrate significant improvements over the state of the art in style transfer. We further show that our framework can be used to successfully complete partial scans with the help of a user provided structural template, coherently propagating scan style across the completed surfaces. [PUBLICATION ABSTRACT]
Author Huang, Haibin
Ma, Chongyang
Sheffer, Alla
Kalogerakis, Evangelos
Wang, Rui
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Copyright 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
2014 The Eurographics Association and John Wiley & Sons Ltd.
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References_xml – reference: [FTP03]  Freeman W.T., Tenenbaum J.B., Pasztor E.C.: Learning style translation for the lines of a drawing. ACM Trans. on Graphics 22, 1 (2003), 33-46. 3, 7.
– reference: [KLM*12]  Kim V.G., Li W., Mitra N., DiVerdi S., Funkhouser T.: Exploring collections of 3D models using fuzzy correspondences. ACM Trans. Graphics 31, 4 (2012), 54:1-54:11. 3.
– reference: [XLZ*10]  Xu K., Li H., Zhang H., Cohen-Or D., Xiong Y., Cheng Z.-Q.: Style-content separation by anisotropic part scales. ACM Trans. Graphics 29, 6 (2010), 184:1-184:10. 1, 3, 7, 9.
– reference: [ACP03]  Allen B., Curless B., Popović Z.: The space of human body shapes: reconstruction and parameterization from range scans. ACM Trans. Graphics 22, 3 (2003), 587-594. 4.
– reference: [XZCOC12]  Xu K., Zhang H., Cohen-Or D., Chen B.: Fit and diverse: Set evolution for inspiring 3D shape galleries. ACM Trans. Graphics 31, 4 (2012), 57:1-57:10. 3.
– reference: [SFCH12]  Shen C.-H., Fu H., Chen K., Hu S.-M.: Structure recovery by part assembly. ACM Trans. Graphics 31, 6 (2012), 180:1-180:11. 3.
– reference: [KCKK12]  Kalogerakis E., Chaudhuri S., Koller D., Koltun V.: A probabilistic model for component-based shape synthesis. ACM Trans. Graphics 31, 4 (2012), 55:1-55:11. 3.
– reference: [ROA*13]  Rustamov R.M., Ovsjanikov M., Azencot O., Ben-Chen M., Chazal F., Guibas L.: Map-based exploration of intrinsic shape differences and variability. ACM Trans. Graph. 32, 4 (2013), 72:1-72:12. 3.
– reference: [BWS10]  Bokeloh M., Wand M., Seidel H.-P.: A connection between partial symmetry and inverse procedural modeling. ACM Trans. Graphics 29 (2010), 104:1-104:10. 3.
– reference: [vKZHCO11]  van Kaick O., Zhang H., Hamarneh G., Cohen-Or D.: A survey on shape correspondence. Computer Graphics Forum 30, 6 (2011), 1681-1707. 3, 4.
– reference: [MGP06]  Mitra N.J., Guibas L.J., Pauly M.: Partial and approximate symmetry detection for 3D geometry. ACM Trans. Graphics 25, 3 (2006), 560-568. 3, 5.
– reference: [SP04]  Sumner R.W., Popović J.: Deformation transfer for triangle meshes. ACM Trans. Graphics 23, 3 (2004), 399-405. 3, 4.
– reference: [PMW*08]  Pauly M., Mitra N.J., Wallner J., Pottmann H., Guibas L.J.: Discovering structural regularity in 3D geometry. ACM Trans. Graphics 27, 3 (2008), 43:1-43:11. 3.
– reference: [CKGK11]  Chaudhuri S., Kalogerakis E., Guibas L., Koltun V.: Probabilistic reasoning for assembly-based 3D modeling. ACM Trans. Graphics 30, 4 (2011), 35:1-35:10. 3.
– reference: [DOIB12]  Delong A., Osokin A., Isack H.N., Boykov Y.: Fast approximate energy minimization with label costs. Int'l Journal of Computer Vision 96, 1 (2012), 1-27. 6.
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Snippet Style transfer aims to apply the style of an exemplar model to a target one, while retaining the target's structure. The main challenge in this process is to...
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SubjectTerms Algorithms
Analogies
Analysis
and object representations
Categories and Subject Descriptors (according to ACM CCS)
Cognitive tasks
Coherence
Computer graphics
Human
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Curve
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Curve, surface, solid, and object representations
Optimization
Queries
solid
Studies
surface
Three dimensional
Transformations
Title Analogy-driven 3D style transfer
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Volume 33
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