TY - GEN
T1 - Skelet Sekans ile aret Dili Videosu Sentezleme
AU - Gencoglu, Sinan
AU - Keles, Hacer Yalim
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/5
Y1 - 2020/10/5
N2 - Generative Adversarial Networks (GANs) enable generating realistic synthetic images. However, majority of the research in this domain focus on image-to-image synthesis problem. The aim of this study is to develop a model that encodes high quality video frames, with true motion dynamics, using only a reference image frame and a skeleton sequence. In this context, Ankara University Turkish Sign Language dataset is used to synthesize new sign videos using a given signer frame as a reference and a skeleton stream. To solve this challenging problem, a conditional generative adversarial network (GAN) is designed, where skeletal data is used as a condition. Using the trained model, we are able to generate sign video streams with the given signer, where the motion dynamics are successfully and fluently encoded in the video. Moreover, we evaluated the quality of the generated images using Fr chet Inception Distance (FID) metric; the FID score is 26.
AB - Generative Adversarial Networks (GANs) enable generating realistic synthetic images. However, majority of the research in this domain focus on image-to-image synthesis problem. The aim of this study is to develop a model that encodes high quality video frames, with true motion dynamics, using only a reference image frame and a skeleton sequence. In this context, Ankara University Turkish Sign Language dataset is used to synthesize new sign videos using a given signer frame as a reference and a skeleton stream. To solve this challenging problem, a conditional generative adversarial network (GAN) is designed, where skeletal data is used as a condition. Using the trained model, we are able to generate sign video streams with the given signer, where the motion dynamics are successfully and fluently encoded in the video. Moreover, we evaluated the quality of the generated images using Fr chet Inception Distance (FID) metric; the FID score is 26.
KW - Generative adversarial networks
KW - conditional generative adversarial networks
KW - convolutional neural networks
KW - video to video synthesis.
UR - https://www.scopus.com/pages/publications/85100307171
U2 - 10.1109/SIU49456.2020.9302436
DO - 10.1109/SIU49456.2020.9302436
M3 - Konferans katkısı
AN - SCOPUS:85100307171
T3 - 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
BT - 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th Signal Processing and Communications Applications Conference, SIU 2020
Y2 - 5 October 2020 through 7 October 2020
ER -