지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.5
- 수록면
- 563 - 576 (14page)
- DOI
- 10.9717/kmms.2024.27.5.563
이용수
초록· 키워드
오류제보하기
In this paper, we propose a novel generator architecture to translate a selfie photo into an animation face image in an unsupervised way. Recent face translation works often fail to preserve the characteristics of facial parts of selfie photos while transferring to animation images. To cope with this, the proposed method develops new Generative Adversarial Network (GAN) architecture composed of (1) simple cycle contents loss, (2) multi-scale assisted self-attention, and (3) adaptive feature fusion. The goal of using cycle contents loss is to make the GAN preserve a wide range of selfie image contents, which includes hair shape, facial expression and shape of face. In addition, multi-scale assisted self-attention complements the existing attention by using its various scales of self-attentions. Adaptive feature fusion helps model to understand the importance of each self-attention map among the multiple self-attention maps generated by the multi-scale self-attention module. Our extensive and comparative experiments on Selfie2Anime and Photo2Anime datasets have been performed to demonstrate the effectiveness of our method over other state-of-the-art methods.
목차
ABSTRACT
1. 서론
2. 연구 배경
3. 제안한 방법
4. 실험 결과 및 고찰
5. 결론
REFERENCE
참고문헌 (18)
참고문헌 신청
J. Qiu and K. Xie, “A GAN-Based Motion Blurred Image Restoration Algorithm,” 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), pp. 211-215, 2019.
H. Ha and B.Y. Hwang, “Enhancement Method of CCTV Video Quality Based on SRGAN,” Journal of Korea Multimedia Society, Vol. 21, No. 9, pp. 1027-1034, 2018.
X. Liu, Z. Gao, and B. M. Chen, “MLFcGAN: Multilevel Feature Fusion-Based Conditional GAN for Underwater Image Color Correction,” IEEE Geoscience and Remote Sensing Letters, Vol. 17, No. 9, pp. 1488-1492, 2019.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., “Generative Adversarial Nets,” Advances in Neural Information Processing Systems, Vol. 27, pp. 139-144, 2014.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, et al., “Attention is All You Need,” Advances in Neural Information Processing Systems, Vol. 30, pp. 6000-6010, 2017.
H. Ha and B.Y. Hwang, “Enhancement Method of CCTV Video Quality Based on SRGAN,” Journal of Korea Multimedia Society, Vol. 21, No. 9, pp. 1027-1034, 2018.
X. Liu, Z. Gao, and B. M. Chen, “MLFcGAN: Multilevel Feature Fusion-Based Conditional GAN for Underwater Image Color Correction,” IEEE Geoscience and Remote Sensing Letters, Vol. 17, No. 9, pp. 1488-1492, 2019.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., “Generative Adversarial Nets,” Advances in Neural Information Processing Systems, Vol. 27, pp. 139-144, 2014.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, et al., “Attention is All You Need,” Advances in Neural Information Processing Systems, Vol. 30, pp. 6000-6010, 2017.
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