Deep Portrait Image Completion and Extrapolation
نویسندگان
چکیده
منابع مشابه
Deep Automatic Portrait Matting
We propose an automatic image matting method for portrait images. This method does not need user interaction, which was however essential in most previous approaches. In order to accomplish this goal, a new end-to-end convolutional neural network (CNN) based framework is proposed taking the input of a portrait image. It outputs the matte result. Our method considers not only image semantic pred...
متن کاملDeep Depth Completion of a Single RGB-D Image
The goal of our work is to complete the depth channel of an RGB-D image. Commodity-grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant surfaces. To address this problem, we train a deep network that takes an RGB image as input and predicts dense surface normals and occlusion boundaries. Those predictions are then combined with raw depth observations provide...
متن کاملSketch-to-Image Generation Using Deep Contextual Completion
When the input to pix2pix translation [9] is a badly drawn sketch, the output follows the input edges due to the strict alignment imposed by the translation process. In this paper we propose sketch-to-image generation, where the output edges do not necessarily follow the input edges. We address the image generation problem using a novel joint image completion approach, where the sketch provides...
متن کاملSpatio-Temporal Image Boundary Extrapolation
Boundary prediction in images as well as video has been a very active topic of research and organizing visual information into boundaries and segments is believed to be a corner stone of visual perception. While prior work has focused on predicting boundaries for observed frames, our work aims at predicting boundaries of future unobserved frames. This requires our model to learn about the fate ...
متن کاملLong-Term Image Boundary Extrapolation
Boundary prediction in images and videos has been a very active topic of research and organizing visual information into boundaries and segments is believed to be a corner stone of visual perception. While prior work has focused on predicting boundaries for observed frames, our work aims at predicting boundaries of future unobserved frames. This requires our model to learn about the fate of bou...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 2020
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2019.2945866