15 citations · 28 across the 6 of their papers we have counts for
4 papers · 1 filter
Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data
Geonmo Gu, Seong Tae Kim, Kihyun Kim +2
In face-related applications with a public available dataset, synthesizing non-linear facial variations (e.g., facial expression, head-pose, illumination, etc.) through a generativ…
Dynamics Transfer GAN: Generating Video by Transferring Arbitrary Temporal Dynamics from a Source Video to a Single Target Image
Wissam J. Baddar, Geonmo Gu, Sangmin Lee +1
In this paper, we propose Dynamics Transfer GAN; a new method for generating video sequences based on generative adversarial learning. The spatial constructs of a generated video s…
Learning Spatio-temporal Features with Partial Expression Sequences for on-the-Fly Prediction
Wissam J. Baddar, Yong Man Ro
Spatio-temporal feature encoding is essential for encoding facial expression dynamics in video sequences. At test time, most spatio-temporal encoding methods assume that a temporal…
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN
Tae Kwan Lee, Wissam J. Baddar, Seong Tae Kim +1
In convolutional neural networks (CNNs), the filter grouping in convolution layers is known to be useful to reduce the network parameter size. In this paper, we propose a new logar…