119 citations · 133 across the 7 of their papers we have counts for
9 papers · 1 filter
Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks
Guillermo Carbajal, Patricia Vitoria, José Lezama +1
In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp im…
MaskSketch: Unpaired Structure-guided Masked Image Generation
Dina Bashkirova, Jose Lezama, Kihyuk Sohn +2
Recent conditional image generation methods produce images of remarkable diversity, fidelity and realism. However, the majority of these methods allow conditioning only on labels o…
Muse: Text-To-Image Generation via Masked Generative Transformers
Huiwen Chang, Han Zhang, Jarred Barber +9
We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregres…
Scaling Painting Style Transfer
Bruno Galerne, Lara Raad, José Lezama +1
Neural style transfer (NST) is a deep learning technique that produces an unprecedentedly rich style transfer from a style image to a content image. It is particularly impressive w…
Visual Prompt Tuning for Generative Transfer Learning
Kihyuk Sohn, Yuan Hao, José Lezama +5
Transferring knowledge from an image synthesis model trained on a large dataset is a promising direction for learning generative image models from various domains efficiently. Whil…
Improved Masked Image Generation with Token-Critic
José Lezama, Huiwen Chang, Lu Jiang +1
Non-autoregressive generative transformers recently demonstrated impressive image generation performance, and orders of magnitude faster sampling than their autoregressive counterp…