2.1k citations · 3.3k across the 18 of their papers we have counts for
10 papers · 1 filter
TryOnDiffusion: A Tale of Two UNets
Luyang Zhu, Dawei Yang, Tyler Zhu +5
Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person. A key challenge…
Novel View Synthesis with Diffusion Models
Daniel Watson, William Chan, Ricardo Martin-Brualla +3
We present 3DiM, a diffusion model for 3D novel view synthesis, which is able to translate a single input view into consistent and sharp completions across many views. The core com…
Imagen Video: High Definition Video Generation with Diffusion Models
Jonathan Ho, William Chan, Chitwan Saharia +8
We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models. Given a text prompt, Imagen Video generates high definition videos…
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Chitwan Saharia, William Chan, Saurabh Saxena +11
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large tran…
Decoder Denoising Pretraining for Semantic Segmentation
Emmanuel Brempong Asiedu, Simon Kornblith, Ting Chen +3
Semantic segmentation labels are expensive and time consuming to acquire. Hence, pretraining is commonly used to improve the label-efficiency of segmentation models. Typically, the…
Why Do Better Loss Functions Lead to Less Transferable Features?
Simon Kornblith, Ting Chen, Honglak Lee +1
Previous work has proposed many new loss functions and regularizers that improve test accuracy on image classification tasks. However, it is not clear whether these loss functions…