TY - GEN
T1 - SimpModeling
T2 - 34th Annual ACM Symposium on User Interface Software and Technology (UIST 2021)
AU - Luo, Zhongjin
AU - Zhou, Jie
AU - Zhu, Heming
AU - Du, Dong
AU - Han, Xiaoguang
AU - Fu, Hongbo
PY - 2021
Y1 - 2021
N2 - Head shapes play an important role in 3D character design. In this work, we propose SimpModeling, a novel sketch-based system for helping users, especially amateur users, easily model 3D animalmorphic heads - a prevalent kind of heads in character design. Although sketching provides an easy way to depict desired shapes, it is challenging to infer dense geometric information from sparse line drawings. Recently, deepnet-based approaches have been taken to address this challenge and try to produce rich geometric details from very few strokes. However, while such methods reduce users' workload, they would cause less controllability of target shapes. This is mainly due to the uncertainty of the neural prediction. Our system tackles this issue and provides good controllability from three aspects: 1) we separate coarse shape design and geometric detail specification into two stages and respectively provide different sketching means; 2) in coarse shape designing, sketches are used for both shape inference and geometric constraints to determine global geometry, and in geometric detail crafting, sketches are used for carving surface details; 3) in both stages, we use the advanced implicit-based shape inference methods, which have strong ability to handle the domain gap between freehand sketches and synthetic ones used for training. Experimental results confirm the effectiveness of our method and the usability of our interactive system. We also contribute to a dataset of high-quality 3D animal heads, which are manually created by artists.
AB - Head shapes play an important role in 3D character design. In this work, we propose SimpModeling, a novel sketch-based system for helping users, especially amateur users, easily model 3D animalmorphic heads - a prevalent kind of heads in character design. Although sketching provides an easy way to depict desired shapes, it is challenging to infer dense geometric information from sparse line drawings. Recently, deepnet-based approaches have been taken to address this challenge and try to produce rich geometric details from very few strokes. However, while such methods reduce users' workload, they would cause less controllability of target shapes. This is mainly due to the uncertainty of the neural prediction. Our system tackles this issue and provides good controllability from three aspects: 1) we separate coarse shape design and geometric detail specification into two stages and respectively provide different sketching means; 2) in coarse shape designing, sketches are used for both shape inference and geometric constraints to determine global geometry, and in geometric detail crafting, sketches are used for carving surface details; 3) in both stages, we use the advanced implicit-based shape inference methods, which have strong ability to handle the domain gap between freehand sketches and synthetic ones used for training. Experimental results confirm the effectiveness of our method and the usability of our interactive system. We also contribute to a dataset of high-quality 3D animal heads, which are manually created by artists.
KW - 3D modeling interface
KW - datasets
KW - implicit fields
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85118226124&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85118226124&origin=recordpage
U2 - 10.1145/3472749.3474791
DO - 10.1145/3472749.3474791
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781450386357
T3 - UIST - Proceedings of the Annual ACM Symposium on User Interface Software and Technology
SP - 854
EP - 863
BT - UIST'21 - Proceedings of the 34th Annual ACM Symposium on User Interface Software and Technology
PB - Association for Computing Machinery
CY - New York
Y2 - 10 October 2021 through 14 October 2021
ER -