26 citations · 78 across the 12 of their papers we have counts for
15 papers
Contrastive Learning for Diverse Disentangled Foreground Generation
Yuheng Li, Yijun Li, Jingwan Lu +3
We introduce a new method for diverse foreground generation with explicit control over various factors. Existing image inpainting based foreground generation methods often struggle…
Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans from a Single Camera
Jae Shin Yoon, Duygu Ceylan, Tuanfeng Y. Wang +4
Appearance of dressed humans undergoes a complex geometric transformation induced not only by the static pose but also by its dynamics, i.e., there exists a number of cloth geometr…
InsetGAN for Full-Body Image Generation
Anna Frühstück, Krishna Kumar Singh, Eli Shechtman +3
While GANs can produce photo-realistic images in ideal conditions for certain domains, the generation of full-body human images remains difficult due to the diversity of identities…
Collaging Class-specific GANs for Semantic Image Synthesis
Yuheng Li, Yijun Li, Jingwan Lu +3
We propose a new approach for high resolution semantic image synthesis. It consists of one base image generator and multiple class-specific generators. The base generator generates…
Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN
Badour AlBahar, Jingwan Lu, Jimei Yang +3
We present an algorithm for re-rendering a person from a single image under arbitrary poses. Existing methods often have difficulties in hallucinating occluded contents photo-reali…
Few-shot Image Generation via Cross-domain Correspondence
Utkarsh Ojha, Yijun Li, Jingwan Lu +4
Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large sourc…