Data-driven Suggestions for Portrait Posing

Hongbo Fu   Xiaoguang Han   Phan Quoc Huy 

The School of Creative Media, City University of Hong Kong   


Data-driven Suggestions for Portrait Posing

With respect to the current pose of a subject being photographed (a) or a set of already-captured portraits (d), our technique automatically provides a ranked list of pose suggestions, which can serve as either visual guidance (b) or stimulate creativity (e) for portrait photographers. Our tool makes even amateur photographers create aesthetically pleasing portraits with great diversity (c and f).


Best Demo Award (voted by attendees)!

This work introduces an easy-to-use creativity support tool for portrait posing, which is an important but challenging problem in portrait photography. While it is well known that a collection of sample poses is a source of inspiration, manual browsing is currently the only option to identify a desired pose from a possibly large collection of poses. With our tool, a photographer is able to easily retrieve desired reference poses as guidance or stimulate creativity. We show how our data-driven suggestions can be used to either refine the current pose of a subject or explore new poses. Our pilot study indicates that unskilled photographers find our data-driven suggestions easy to use and useful, though the role of our suggestions in improving aesthetic quality or pose diversity still needs more investigation. Our work takes the first step of using consumer-level depth sensors towards more intelligent cameras for computational photography.

Download the video (.mp4; 78M)


Hongbo Fu, Xiaoguang Han, and Phan Quoc Huy. Data-driven suggestions for portrait posing. ACM SIGGRAPH Asia 2013, Technical Briefs. PDF.

Hongbo Fu. Xiaoguang Han, and Phan Quoc Huy. Data-driven suggestions for portrait posing. ACM SIGGRAPH Asia 2013, Emerging Technologies. PDF

author = {Hongbo Fu and Xiaoguang Han and Phan Quoc Huy},
title = {Data-driven suggestions for portrait posing},
booktitle = {ACM SIGGRAPH Asia 2013, Technical Briefs},
year = {2013},


We thank the reviewers for their constructive comments, the user study participants (namely, Victor Dibia, Audrey Samson, Guolei Zhang, Pengfei Xu, Qiaochu Mei, Qingkun Su, Xiaoyan Shen, Wanqi Li, Xiaozhu Zhang, Jiayue Yu, Xiaoyong Shen, Siyu Li) for their time, and Michael Brown for video narration. We are grateful to Flickr for allowing us to download so many images. This work was substantially supported by the Seed Grant from the City University of Hong Kong (No. 7003058).