TY - GEN
T1 - Apparel Generation via Cluster-Indexed Global and Local Feature Representations
AU - Gu, Chunzhi
AU - Huang, Zhengyu
AU - Li, Sicheng
AU - Xie, Haoran
AU - Yang, Xi
AU - Zhang, Chao
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/13
Y1 - 2020/10/13
N2 - Apparel design requires expertise in aesthetics, which is challenging for non-professional general users. Inspired by the recent advances in data science, in this paper we address the task of apparel generation in a simple way by leveraging a deep neural network model. We propose to generate clothes through three selection steps from the big picture (e.g., type of clothes) to the details (e.g., color) by varying the cluster ID and latent variables. Users can go through these steps to achieve an ideal design. Experiments on a publicly available dateset demonstrate the effectiveness of our method.
AB - Apparel design requires expertise in aesthetics, which is challenging for non-professional general users. Inspired by the recent advances in data science, in this paper we address the task of apparel generation in a simple way by leveraging a deep neural network model. We propose to generate clothes through three selection steps from the big picture (e.g., type of clothes) to the details (e.g., color) by varying the cluster ID and latent variables. Users can go through these steps to achieve an ideal design. Experiments on a publicly available dateset demonstrate the effectiveness of our method.
UR - http://www.scopus.com/inward/record.url?scp=85099343993&partnerID=8YFLogxK
U2 - 10.1109/GCCE50665.2020.9291984
DO - 10.1109/GCCE50665.2020.9291984
M3 - 会議への寄与
AN - SCOPUS:85099343993
T3 - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
SP - 218
EP - 219
BT - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE Global Conference on Consumer Electronics, GCCE 2020
Y2 - 13 October 2020 through 16 October 2020
ER -