Publication

Predicting Deeper into the Future of Semantic Segmentation

International Conference on Computer Vision (ICCV)


Abstract

The ability to predict and therefore to anticipate the future is an important attribute of intelligence. It is also of utmost importance in real-time systems, e.g. in robotics or autonomous driving, which depend on visual scene understanding for decision making. While prediction of the raw RGB pixel values in future video frames has been studied in previous work, here we introduce the novel task of predicting semantic segmentations of future frames. Given a sequence of video frames, our goal is to predict segmentation maps of not yet observed video frames that lie up to a second or further in the future. We develop an autoregressive convolutional neural network that learns to iteratively generate multiple frames. Our results on the Cityscapes dataset show that directly predicting future segmentations is substantially better than predicting and then segmenting future RGB frames. Prediction results up to half a second in the future are visually convincing and are much more accurate than those of a baseline based on warping semantic segmentations using optical flow.

Related Publications

All Publications

October 30, 2017

NextSegmPred

No Authors Listed

CVPR - June 19, 2021

SimPoE: Simulated Character Control for 3D Human Pose Estimation

Ye Yuan, Shih-En Wei, Tomas Simon, Kris Kitani, Jason Saragih

ICLR - May 3, 2021

Support-Set Bottlenecks for Video-Text Representation Learning

Mandela Patrick, Po-Yao Huang, Florian Metze, Andrea Vedaldi, Alexander Hauptmann, Yuki M. Asano, João Henriques

CVPR - June 19, 2021

Pixel Codec Avatars

Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando De la Torre, Yaser Sheikh

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy