Learning an interpretable stylized subspace for 3D-aware animatable artforms

Learning an interpretable stylized subspace for 3D-aware animatable artforms

Throughout history, static paintings have captivated viewers within display frames, yet the possibility of making these masterpieces vividly interactive remains intriguing. This research paper introduces 3DArtmator, a novel approach that aims to represent artforms in a highly interpretable stylized...

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Bibliographic Details
Main Authors: ZHENG, Chenxi, LIU, Bangzhen, XU, Xuemiao, ZHANG, Huaidong, HE, Shengfeng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
3D-aware GANs
Adaptation models
facial attribute editing
Image reconstruction
Painting
stylized animation
Three-dimensional displays
Training
Graphics and Human Computer Interfaces
Software Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/8697
https://ink.library.smu.edu.sg/context/sis_research/article/9700/viewcontent/LearningInterpretableStylizedAnimatable_Artforms_av.pdf
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Institution: Singapore Management University
Language: English

Internet

https://ink.library.smu.edu.sg/sis_research/8697
https://ink.library.smu.edu.sg/context/sis_research/article/9700/viewcontent/LearningInterpretableStylizedAnimatable_Artforms_av.pdf

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