Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

3D corrective nose reconstruction from a single image

Tang, Yanlong, Zhang, Yun, Han, Xiaoguang, Zhang, Fang-Lue, Lai, YuKun ORCID: and Tong, Ruofeng 2022. 3D corrective nose reconstruction from a single image. Computational Visual Media 8 (2) , pp. 225-237. 10.1007/s41095-021-0237-5

[thumbnail of 3DNoseCorrection.pdf]
PDF - Published Version
Available under License Creative Commons Attribution.

Download (4MB) | Preview


There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature. When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and expression. In this paper, we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image, where 3D and 2D nose curve correspondences are adaptively updated and refined. We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences, and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result. A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints. Experimental results show the advantages of our method for 3D nose reconstruction over existing methods.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License
Publisher: SpringerOpen
ISSN: 2096-0433
Date of First Compliant Deposit: 6 December 2021
Date of Acceptance: 29 April 2021
Last Modified: 10 May 2023 10:20

Citation Data

Cited 3 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics