726 citations · 765 across the 4 of their papers we have counts for
4 papers
NCHO: Unsupervised Learning for Neural 3D Composition of Humans and Objects
Taeksoo Kim, Shunsuke Saito, Hanbyul Joo
Deep generative models have been recently extended to synthesizing 3D digital humans. However, previous approaches treat clothed humans as a single chunk of geometry without consid…
Semi-supervised Feature-Level Attribute Manipulation for Fashion Image Retrieval
Minchul Shin, Sanghyuk Park, Taeksoo Kim
With a growing demand for the search by image, many works have studied the task of fashion instance-level image retrieval (FIR). Furthermore, the recent works introduce a concept o…
Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks
Taeksoo Kim, Byoungjip Kim, Moonsu Cha +1
Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfe…
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim +2
While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and need…