110 citations · 190 across the 6 of their papers we have counts for
9 papers
Putting People in Their Place: Affordance-Aware Human Insertion into Scenes
Sumith Kulal, Tim Brooks, Alex Aiken +5
We study the problem of inferring scene affordances by presenting a method for realistically inserting people into scenes. Given a scene image with a marked region and an image of…
InstructPix2Pix: Learning to Follow Image Editing Instructions
Tim Brooks, Aleksander Holynski, Alexei A. Efros
We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions…
Learning to Learn with Generative Models of Neural Network Checkpoints
William Peebles, Ilija Radosavovic, Tim Brooks +2
We explore a data-driven approach for learning to optimize neural networks. We construct a dataset of neural network checkpoints and train a generative model on the parameters. In…
Studying Bias in GANs through the Lens of Race
Vongani H. Maluleke, Neerja Thakkar, Tim Brooks +5
In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of their training datasets. By examining and controlling…
Generating Long Videos of Dynamic Scenes
Tim Brooks, Janne Hellsten, Miika Aittala +6
We present a video generation model that accurately reproduces object motion, changes in camera viewpoint, and new content that arises over time. Existing video generation methods…
Hallucinating Pose-Compatible Scenes
Tim Brooks, Alexei A. Efros
What does human pose tell us about a scene? We propose a task to answer this question: given human pose as input, hallucinate a compatible scene. Subtle cues captured by human pose…
Handheld Mobile Photography in Very Low Light
Orly Liba, Kiran Murthy, Yun-Ta Tsai +10
Taking photographs in low light using a mobile phone is challenging and rarely produces pleasing results. Aside from the physical limits imposed by read noise and photon shot noise…
Unprocessing Images for Learned Raw Denoising
Tim Brooks, Ben Mildenhall, Tianfan Xue +3
Machine learning techniques work best when the data used for training resembles the data used for evaluation. This holds true for learned single-image denoising algorithms, which a…
Learning to Synthesize Motion Blur
Tim Brooks, Jonathan T. Barron
We present a technique for synthesizing a motion blurred image from a pair of unblurred images captured in succession. To build this system we motivate and design a differentiable…