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Found 5 papers, 2 papers with code
GALA: Generating Animatable Layered Assets from a Single Scan
Through a series of decomposition steps, we obtain multiple layers of 3D assets in a shared canonical space normalized in terms of poses and human shapes, hence supporting effortless composition to novel identities and reanimation with novel poses.
NCHO: Unsupervised Learning for Neural 3D Composition of Humans and Objects
Our compositional model is interaction-aware, meaning the spatial relationship between humans and objects, and the mutual shape change by physical contact is fully incorporated.
Semi-supervised Feature-Level Attribute Manipulation for Fashion Image Retrieval
FAM is a challenging task in that the attributes are hard to define, and the unique characteristics of a query are hard to be preserved.
Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks
To address the issue, we propose an unsupervised method to learn to transfer visual attribute.
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations.
Ranked #10 on
Facial Expression Translation
on CelebA
