Transient Attributes for High-Level Understanding
and Editing of Outdoor Scenes

People

Pierre-Yves Laffont
Zhile Ren
Xiaofeng Tao
Chao Qian
James Hays

Abstract

We live in a dynamic visual world where the appearance of scenes changes dramatically from hour to hour or season to season. In this work we study “transient scene attributes” – high level properties which affect scene appearance, such as “snow”, “autumn”, “dusk”, “fog”. We define 40 transient attributes and use crowdsourcing to annotate thousands of images from 101 webcams. We use this “transient attribute database” to train regressors that can predict the presence of attributes in novel images. We demonstrate a photo organization method based on predicted attributes. Finally we propose a high-level image editing method which allows a user to adjust the attributes of a scene, e.g. change a scene to be “snowy” or “sunset”. To support attribute manipulation we introduce a novel appearance transfer technique which is simple and fast yet competitive with the state-of-the-art. We show that we can convincingly modify many transient attributes in outdoor scenes.

SIGGRAPH 2014 paper

The paper can be downloaded from ACM Digital Library. We provide supplementary materials in addition to the paper:

BibTeX citation

@article {Laffont14,
    title = {Transient Attributes for High-Level Understanding and Editing of Outdoor Scenes},
    author = {Pierre-Yves Laffont and Zhile Ren and Xiaofeng Tao and Chao Qian and James Hays},
    journal = {ACM Transactions on Graphics (proceedings of SIGGRAPH 2014)},
    volume = {33},
    number = {4},
    year = {2014}
}

SIGGRAPH slides

We presented our work at SIGGRAPH 2014 in Vancouver. We share our slides for scientific dissemination, along with our "fast-forward" SIGGRAPH video.

Web-based user interfaces

We show examples of user interfaces that leverage our transient attributes on the following pages:

These pages have been tested with Chrome v35 (recommended), Firefox v29, and Internet Explorer v10.

Transient Attributes Database

Our dataset contains 8571 annotated images from 101 webcams, and can be downloaded from the links below:

Code for our Transient Attributes regressors

We release our trained regressors that can predict transient attribute labels for an outdoor input image. This code was used to estimate the attribute labels for photo collections in the two web interfaces above.

Funding

This research is supported by: