#ELEMENTS OF STYLE ARCHIVE#

Geoff Nunberg may have said it best: 'The weird thing is to see rules like these passed down as traditional linguistic wisdom. I devoted some of my grammar thesis to criticizing this book, and it was time well spent. Take that edict that you ought to say '10 persons' rather than '10 people.' You can still find it in the recent editions of Strunk This book is good for the following things: Using as a coaster for cold drinks I devoted some of my grammar thesis to criticizing this book, and it was time well spent. We thank the anonymous reviewers for their comments.This book is good for the following things: 1. We thank Nicholas Vining and Mikhail Bessmeltsev for their valuable help in editing the paper and the video.

Sheffer gratefully acknowledges support from NSERC and GRAND NCE. Kalogerakis gratefully acknowledges support from NSF (CHS-1422441). This data set is provided for the convenience of academic research only The user study data was obtained by us via Amazon Mechanical Turk service and it is provided freely. The following files contain the slides presented in SIGGRAPH 2015 at Los Angeles.Īll 3D shape models are downloaded from the Internet and the original authors hold the copyright of the models. Please read the readme file within the archives for more details.

#ELEMENTS OF STYLE ARCHIVE#

Archive Data-demo.7z contains minimum data to run our pipeline. Archive Data-all.7z contains all data used in our experiments including shape models and user study responses. The following archives contain data set used in our algorithm. Please read the readme file within the archive for more details. The following archive contains source codes for our algorithm. The following archive contains queries generated for our data sets and learned weights for elementary distances and saliences. 'Elements of Style: Learning Perceptual Shape Style Similarity',ĪCM Transactions on Graphics (Proc. Zhaoliang Lun, Evangelos Kalogerakis, Alla Sheffer, We train and validate our method on this dataset, showing it to successfully predict relative style similarity with 90% accuracy based on 10-fold cross-validation.

To achieve this consistency we employ crowd-sourcing to quantify the different components of our measure and learn the relative perceptual importance of a range of elementary shape distances and other parameters used in our measurement from 50K responses to cross-structure style similarity queries provided by over 2500 participants.

We translate these observations into an algorithmic measure by quantifying in geometric terms what makes geometric elements be perceived as similarly shaped, employing this quantification to detect similar style elements on the analyzed shapes, and finally collating the element-level geometric similarity measurements into an object-level style measure consistent with human perception. Our measure is inspired by observations about style-similarity in art history literature, which point to the presence of similarly shaped, salient, geometric elements as a key indicator of stylistic similarity between structurally different objects. In contrast, we introduce and successfully validate the first structure-transcending style similarity measure, one closely aligned with the human perception of style similarity. Previous work in style analysis focused on shapes within the same class, and leveraged structural similarity between these shapes to facilitate analysis. An algorithmically computed style similarity measure that mimics human perception, can benefit a range of computer graphics applications. The human perception of stylistic similarity transcends structure and function - for instance, a bed and a dresser may share a common style.

Elements of style