Abstract
We present an object relighting system that allows an artist to select an object from an image and insert it into a target scene. Through simple interactions, the system can adjust illumination on the inserted object so that it appears naturally in the scene. To support image-based relighting, we build object model from the image, and propose a perceptually-inspired approximate shading model for the relighting. It decomposes the shading field into (a) a rough shape term that can be reshaded, (b) a parametric shading detail that encodes missing features from the first term, and (c) a geometric detail term that captures fine-scale material properties. With this decomposition, the shading model combines 3D rendering and image-based composition and allows more flexible compositing than image-based methods. Quantitative evaluation and a set of user studies suggest our method is a promising alternative to existing methods of object insertion.
| Language | English (US) |
|---|---|
| Pages | 22-37 |
| Number of pages | 16 |
| Journal | International Journal of Computer Vision |
| Volume | 127 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 15 2019 |
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Keywords
- Image relighting
- Image-based modeling
- Object insertion
- Shading model
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Artificial Intelligence
Cite this
An Approximate Shading Model with Detail Decomposition for Object Relighting. / Liao, Zicheng; Karsch, Kevin; Zhang, Hongyi; Forsyth, David Alexander.
In: International Journal of Computer Vision, Vol. 127, No. 1, 15.01.2019, p. 22-37.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An Approximate Shading Model with Detail Decomposition for Object Relighting
AU - Liao, Zicheng
AU - Karsch, Kevin
AU - Zhang, Hongyi
AU - Forsyth, David Alexander
PY - 2019/1/15
Y1 - 2019/1/15
N2 - We present an object relighting system that allows an artist to select an object from an image and insert it into a target scene. Through simple interactions, the system can adjust illumination on the inserted object so that it appears naturally in the scene. To support image-based relighting, we build object model from the image, and propose a perceptually-inspired approximate shading model for the relighting. It decomposes the shading field into (a) a rough shape term that can be reshaded, (b) a parametric shading detail that encodes missing features from the first term, and (c) a geometric detail term that captures fine-scale material properties. With this decomposition, the shading model combines 3D rendering and image-based composition and allows more flexible compositing than image-based methods. Quantitative evaluation and a set of user studies suggest our method is a promising alternative to existing methods of object insertion.
AB - We present an object relighting system that allows an artist to select an object from an image and insert it into a target scene. Through simple interactions, the system can adjust illumination on the inserted object so that it appears naturally in the scene. To support image-based relighting, we build object model from the image, and propose a perceptually-inspired approximate shading model for the relighting. It decomposes the shading field into (a) a rough shape term that can be reshaded, (b) a parametric shading detail that encodes missing features from the first term, and (c) a geometric detail term that captures fine-scale material properties. With this decomposition, the shading model combines 3D rendering and image-based composition and allows more flexible compositing than image-based methods. Quantitative evaluation and a set of user studies suggest our method is a promising alternative to existing methods of object insertion.
KW - Image relighting
KW - Image-based modeling
KW - Object insertion
KW - Shading model
UR - http://www.scopus.com/inward/record.url?scp=85060338599&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060338599&partnerID=8YFLogxK
U2 - 10.1007/s11263-018-1090-6
DO - 10.1007/s11263-018-1090-6
M3 - Article
VL - 127
SP - 22
EP - 37
JO - International Journal of Computer Vision
T2 - International Journal of Computer Vision
JF - International Journal of Computer Vision
SN - 0920-5691
IS - 1
ER -