no code implementations • 26 Jul 2019 • Il Yong Chun, Zhengyu Huang, Hongki Lim, Jeffrey A. Fessler
Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision.
no code implementations • 27 Jan 2022 • Zhengyu Huang, Haoran Xie, Tsukasa Fukusato
We propose an interactive 3D character modeling approach from orthographic drawings (e. g., front and side views) based on 2D-space annotations.
no code implementations • 14 Mar 2022 • Zhengyu Huang, Weizhi Du, Theodore B. Norris
We propose an unsupervised deep learning based method to estimate depth from focal stack camera images.
no code implementations • 13 Jun 2023 • Zhengyu Huang, Haoran Xie, Tsukasa Fukusato, Kazunori Miyata
In the second stage, we simulated the drawing process of the generated images without any additional data (labels) and trained the sketch encoder for incomplete progressive sketches to generate high-quality portrait images with feature alignment to the disentangled representations in the teacher encoder.
1 code implementation • 5 Mar 2021 • Zhengyu Huang, Theodore B. Norris, Panqu Wang
Dense stereo matching with deep neural networks is of great interest to the research community.
1 code implementation • 26 Apr 2021 • Zhengyu Huang, Yichen Peng, Tomohiro Hibino, Chunqi Zhao, Haoran Xie, Tsukasa Fukusato, Kazunori Miyata
In the stage of local guidance, we synthesize detailed portrait images with a deep generative model from user-drawn contour lines, but use the synthesized results as detailed drawing guidance.