TY - GEN
T1 - SHAD3S
T2 - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
AU - Venkataramaiyer, Raghav Brahmadesam
AU - Joshi, Abhishek
AU - Narang, Saisha
AU - Namboodiri, Vinay P.
PY - 2021/1/9
Y1 - 2021/1/9
N2 - Hatching is a common method used by artists to accentuate the third dimension of a sketch, and to illuminate the scene. Our system SHAD3S 1 attempts to compete with a human at hatching generic three-dimensional (3D) shapes, and also tries to assist her in a form exploration exercise. The novelty of our approach lies in the fact that we make no assumptions about the input other than that it represents a 3D shape, and yet, given a contextual information of illumination and texture, we synthesise an accurate hatch pattern over the sketch, without access to 3D or pseudo 3D. In the process, we contribute towards a) a cheap yet effective method to synthesise a sufficiently large high fidelity dataset, pertinent to task; b) creating a pipeline with conditional generative adversarial network (CGAN); and c) creating an interactive utility with GIMP, that is a tool for artists to engage with automated hatching or a form-exploration exercise. User evaluation of the tool suggests that the model performance does generalise satisfactorily over diverse input, both in terms of style as well as shape. A simple comparison of inception scores suggest that the generated distribution is as diverse as the ground truth.
AB - Hatching is a common method used by artists to accentuate the third dimension of a sketch, and to illuminate the scene. Our system SHAD3S 1 attempts to compete with a human at hatching generic three-dimensional (3D) shapes, and also tries to assist her in a form exploration exercise. The novelty of our approach lies in the fact that we make no assumptions about the input other than that it represents a 3D shape, and yet, given a contextual information of illumination and texture, we synthesise an accurate hatch pattern over the sketch, without access to 3D or pseudo 3D. In the process, we contribute towards a) a cheap yet effective method to synthesise a sufficiently large high fidelity dataset, pertinent to task; b) creating a pipeline with conditional generative adversarial network (CGAN); and c) creating an interactive utility with GIMP, that is a tool for artists to engage with automated hatching or a form-exploration exercise. User evaluation of the tool suggests that the model performance does generalise satisfactorily over diverse input, both in terms of style as well as shape. A simple comparison of inception scores suggest that the generated distribution is as diverse as the ground truth.
UR - http://www.scopus.com/inward/record.url?scp=85116113983&partnerID=8YFLogxK
U2 - 10.1109/WACV48630.2021.00366
DO - 10.1109/WACV48630.2021.00366
M3 - Chapter in a published conference proceeding
AN - SCOPUS:85116113983
SN - 9781665404778
T3 - Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
SP - 3615
EP - 3624
BT - Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
PB - IEEE
CY - U. S. A.
Y2 - 5 January 2021 through 9 January 2021
ER -