Name
Abstract | ||
|---|---|---|
Recently, we have seen a growing trend in the design and fabrication of personalized figurines, created by scanning real people and then physically reproducing miniature statues with 3D printers. This is currently a hot topic both in academia and industry, and the printed figurines are gaining more and more realism, especially with state-of-the-art facial scanning technology improving. However, current systems all contain the same limitation - no previous method is able to suitably capture personalized hair-styles for physical reproduction. Typically, the subject's hair is approximated very coarsely or replaced completely with a template model. In this paper we present the first method for stylized hair capture, a technique to reconstruct an individual's actual hair-style in a manner suitable for physical reproduction. Inspired by centuries-old artistic sculptures, our method generates hair as a closed-manifold surface, yet contains the structural and color elements stylized in a way that captures the defining characteristics of the hair-style. The key to our approach is a novel multi-view stylization algorithm, which extends feature-preserving color filtering from 2D images to irregular manifolds in 3D, and introduces abstract geometric details that are coherent with the color stylization. The proposed technique fits naturally in traditional pipelines for figurine reproduction, and we demonstrate the robustness and versatility of our approach by capturing several subjects with widely varying hair-styles. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2601097.2601133 | ACM Trans. Graph. |
Keywords | Field | DocType |
hair capture,printable figurines,scanning,stylization | Computer vision,Computer graphics (images),Computer science,Stylized fact,Filter (signal processing),Robustness (computer science),Artificial intelligence | Journal |
Volume | Issue | ISSN |
33 | 4 | 0730-0301 |
Citations | PageRank | References |
21 | 0.71 | 30 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose I. Echevarria | 1 | 78 | 9.52 |
| Derek Bradley | 2 | 973 | 45.81 |
| diego gutierrez | 3 | 1263 | 91.03 |
| Thabo Beeler | 4 | 639 | 30.77 |