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Article
Computer Science, Software Engineering
Rameen Abdal et al.
Summary: This article explores the control of attribute generation in GAN latent space and presents StyleFlow as a simple, effective, and robust solution to this issue. Through experiments in face and car latent space, it successfully demonstrates fine-grained disentangled edits along various attributes.
ACM TRANSACTIONS ON GRAPHICS
(2021)
Article
Computer Science, Software Engineering
Omer Tov et al.
Summary: This paper studies the latent space of the StyleGAN unconditional generator and identifies tradeoffs between distortion-editability and distortion-perception within the latent space. Two principles for designing encoders to control the proximity of inversions to the regions originally trained on StyleGAN are suggested. By qualitatively and quantitatively evaluating on challenging domains like cars and horses, the inversion method presented here, along with common editing techniques, achieves superior real-image editing quality with only a minor drop in reconstruction accuracy.
ACM TRANSACTIONS ON GRAPHICS
(2021)
Article
Computer Science, Software Engineering
Yuval Alaluf et al.
Summary: The study introduces an image-to-image translation method for age transformation, which allows for fine-grained control over the generated image by guiding the encoder to generate latent codes corresponding to the desired age. It demonstrates advantages over existing methods in terms of further image editing capabilities and continuous aging process modeling.
ACM TRANSACTIONS ON GRAPHICS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Thao Nguyen et al.
Summary: This study introduces a comprehensive makeup transfer framework that can handle all makeup properties, including color, shape, texture, and location. By introducing new makeup datasets, experimental results show that the framework performs well on different makeup styles.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Qiusheng Huang et al.
Summary: This paper introduces the image-to-image translation model, divided into label-based and reference-based synthesis. By designing encoding modules to compare domain differences and fusing latent codes to reflect domain style differences, both label- and reference-based synthesis are shown to mutually promote each other.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Han Deng et al.
Summary: This paper proposes a Style-based Controllable GAN model to break down the makeup transfer problem into a two-step extraction-assignment process. By encoding component-wise makeup styles, the model shows great flexibility in makeup transfer and superiority even with large spatial misalignment.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Wei Xiong et al.
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2020)
Article
Computer Science, Hardware & Architecture
Shiping Wen et al.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Xun Huang et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Francois Chollet
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Article
Computer Science, Information Systems
Yong-Jin Liu et al.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2014)
