Hallucinating Pose-Compatible Scenes Fall 2021

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. We present a large-scale generative adversarial network for pose-conditioned scene generation.

Learning to Synthesize Motion Blur Fall 2018

CVPR, 2019 (Oral Presentation)

It is difficult to portray a sense of movement in a single image. We present a novel technique to synthesize motion blur, which creates a visual effect to summarize movement, such as portraying a car racing by or the commotion of a busy city intersection. It is also useful to temporally smooth timelapse videos and rendered animations.

Unprocessing Images for Learned Raw Denoising Fall 2018

CVPR, 2019 (Oral Presentation)

Photographs often exhibit noise, especially in low light. While denoise neural networks work well on synthetic inputs, they often fail on real noisy images. We present the new approach to "unprocess" images, which creates more realistic training data and produces state-of-the-art results on real photos.

HDR+ Burst Processing Pipeline Fall 2016

Inspired by the Google Pixel's HDR+ burst photography mode, this technique combines multiple underexposed raw frames to decrease noise, then applies a sequence of standard image processing algorithms.