TY  - GEN
TI  - High-Dynamic-Range Lighting Estimation From Face Portraits.
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
Y1  - 2021/01//
UR  - https://doi.org/10.1109/3DV50981.2020.00045
N2  - We present a CNN-based method for outdoor highdynamic-range (HDR) environment map prediction from low-dynamic-range (LDR) portrait images. Our method relies on two different CNN architectures, one for light encoding and another for face-to-light prediction. Outdoor lighting is characterised by an extremely high dynamic range, and thus our encoding splits the environment map data between low and high-intensity components, and encodes them using tailored representations. The combination of both network architectures constitutes an end-to-end method for accurate HDR light prediction from faces at real-time rates, inaccessible for previous methods which focused on low dynamic range lighting or relied on non-linear optimisation schemes. We train our networks using both real and synthetic images, we compare our light encoding with other methods for light representation, and we analyse our results for light prediction on real images. We show that our predicted HDR environment maps can be used as accurate illumination sources for scene renderings, with potential applications in 3D object insertion for augmented reality.
PB  - IEEE
SP  - 355
A1  - Sztrajman, A
A1  - Neophytou, A
A1  - Weyrich, T
A1  - Sommerlade, E
CY  - Fukuoka, Japan
KW  - Lighting
KW  - 
Faces
KW  - 
Encoding
KW  - 
Training
KW  - 
Three-dimensional displays
KW  - 
Sun
KW  - 
Estimation
EP  - 363
ID  - discovery10123224
AV  - public
ER  -