TY - GEN
T1 - CloTH-VTON
T2 - 15th Asian Conference on Computer Vision, ACCV 2020
AU - Minar, Matiur Rahman
AU - Ahn, Heejune
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Virtual clothing try-on, transferring a clothing image onto a target person image, is drawing industrial and research attention. Both 2D image-based and 3D model-based methods proposed recently have their benefits and limitations. Whereas 3D model-based methods provide realistic deformations of the clothing, it needs a difficult 3D model construction process and cannot handle the non-clothing areas well. Image-based deep neural network methods are good at generating disclosed human parts, retaining the unchanged area, and blending image parts, but cannot handle large deformation of clothing. In this paper, we propose CloTH-VTON that utilizes the high-quality image synthesis of 2D image-based methods and the 3D model-based deformation to the target human pose. For this 2D and 3D combination, we propose a novel 3D cloth reconstruction method from a single 2D cloth image, leveraging a 3D human body model, and transfer to the shape and pose of the target person. Our cloth reconstruction method can be easily applied to diverse cloth categories. Our method produces final try-on output with naturally deformed clothing and preserving details in high resolution.
AB - Virtual clothing try-on, transferring a clothing image onto a target person image, is drawing industrial and research attention. Both 2D image-based and 3D model-based methods proposed recently have their benefits and limitations. Whereas 3D model-based methods provide realistic deformations of the clothing, it needs a difficult 3D model construction process and cannot handle the non-clothing areas well. Image-based deep neural network methods are good at generating disclosed human parts, retaining the unchanged area, and blending image parts, but cannot handle large deformation of clothing. In this paper, we propose CloTH-VTON that utilizes the high-quality image synthesis of 2D image-based methods and the 3D model-based deformation to the target human pose. For this 2D and 3D combination, we propose a novel 3D cloth reconstruction method from a single 2D cloth image, leveraging a 3D human body model, and transfer to the shape and pose of the target person. Our cloth reconstruction method can be easily applied to diverse cloth categories. Our method produces final try-on output with naturally deformed clothing and preserving details in high resolution.
KW - 3D cloth reconstruction
KW - Generative model
KW - Virtual Try-On
UR - http://www.scopus.com/inward/record.url?scp=85103288138&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-69544-6_10
DO - 10.1007/978-3-030-69544-6_10
M3 - Conference contribution
AN - SCOPUS:85103288138
SN - 9783030695439
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 154
EP - 172
BT - Computer Vision – ACCV 2020 - 15th Asian Conference on Computer Vision, 2020, Revised Selected Papers
A2 - Ishikawa, Hiroshi
A2 - Liu, Cheng-Lin
A2 - Pajdla, Tomas
A2 - Shi, Jianbo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 30 November 2020 through 4 December 2020
ER -