8 citations · 12 across the 3 of their papers we have counts for
4 papers
Texturize a GAN Using a Single Image
Pengda Xiang, Sitao Xiang, Yajie Zhao
Can we customize a deep generative model which can generate images that can match the texture of some given image? When you see an image of a church, you may wonder if you can get…
DisUnknown: Distilling Unknown Factors for Disentanglement Learning
Sitao Xiang, Yuming Gu, Pengda Xiang +4
Disentangling data into interpretable and independent factors is critical for controllable generation tasks. With the availability of labeled data, supervision can help enforce the…
One-Shot Identity-Preserving Portrait Reenactment
Sitao Xiang, Yuming Gu, Pengda Xiang +4
We present a deep learning-based framework for portrait reenactment from a single picture of a target (one-shot) and a video of a driving subject. Existing facial reenactment metho…
Learning Formation of Physically-Based Face Attributes
Ruilong Li, Karl Bladin, Yajie Zhao +8
Based on a combined data set of 4000 high resolution facial scans, we introduce a non-linear morphable face model, capable of producing multifarious face geometry of pore-level res…