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13 papers · 2 filters
Structure-Guided Image Completion with Image-level and Object-level Semantic Discriminators
Haitian Zheng, Zhe Lin, Jingwan Lu +8
Structure-guided image completion aims to inpaint a local region of an image according to an input guidance map from users. While such a task enables many practical applications fo…
Multi-Concept Customization of Text-to-Image Diffusion
Nupur Kumari, Bingliang Zhang, Richard Zhang +2
While generative models produce high-quality images of concepts learned from a large-scale database, a user often wishes to synthesize instantiations of their own concepts (for exa…
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…
Text-Free Learning of a Natural Language Interface for Pretrained Face Generators
Xiaodan Du, Raymond A. Yeh, Nicholas Kolkin +2
We propose Fast text2StyleGAN, a natural language interface that adapts pre-trained GANs for text-guided human face synthesis. Leveraging the recent advances in Contrastive Languag…
Controllable Shadow Generation Using Pixel Height Maps
Yichen Sheng, Yifan Liu, Jianming Zhang +6
Shadows are essential for realistic image compositing. Physics-based shadow rendering methods require 3D geometries, which are not always available. Deep learning-based shadow synt…
ARF: Artistic Radiance Fields
Kai Zhang, Nick Kolkin, Sai Bi +4
We present a method for transferring the artistic features of an arbitrary style image to a 3D scene. Previous methods that perform 3D stylization on point clouds or meshes are sen…
BlobGAN: Spatially Disentangled Scene Representations
Dave Epstein, Taesung Park, Richard Zhang +2
We propose an unsupervised, mid-level representation for a generative model of scenes. The representation is mid-level in that it is neither per-pixel nor per-image; rather, scenes…
Any-resolution Training for High-resolution Image Synthesis
Lucy Chai, Michael Gharbi, Eli Shechtman +2
Generative models operate at fixed resolution, even though natural images come in a variety of sizes. As high-resolution details are downsampled away and low-resolution images are…
Neural Neighbor Style Transfer
Nicholas Kolkin, Michal Kucera, Sylvain Paris +3
We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer. Our appro…
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…
CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training
Haitian Zheng, Zhe Lin, Jingwan Lu +7
Recent image inpainting methods have made great progress but often struggle to generate plausible image structures when dealing with large holes in complex images. This is partiall…
Third Time's the Charm? Image and Video Editing with StyleGAN3
Yuval Alaluf, Or Patashnik, Zongze Wu +4
StyleGAN is arguably one of the most intriguing and well-studied generative models, demonstrating impressive performance in image generation, inversion, and manipulation. In this w…
GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding
Yunhan Zhao, Connelly Barnes, Yuqian Zhou +3
Reference-guided image inpainting restores image pixels by leveraging the content from another single reference image. The primary challenge is how to precisely place the pixels fr…