  We present a method to efficiently generate 3D-aware high-resolution images
that are view-consistent across multiple target views. The proposed multiplane
neural radiance model, named GMNR, consists of a novel {\alpha}-guided
view-dependent representation ({\alpha}-VdR) module for learning view-dependent
information. The {\alpha}-VdR module, faciliated by an {\alpha}-guided pixel
sampling technique, computes the view-dependent representation efficiently by
learning viewing direction and position coefficients. Moreover, we propose a
view-consistency loss to enforce photometric similarity across multiple views.
The GMNR model can generate 3D-aware high-resolution images that are
viewconsistent across multiple camera poses, while maintaining the
computational efficiency in terms of both training and inference time.
Experiments on three datasets demonstrate the effectiveness of the proposed
modules, leading to favorable results in terms of both generation quality and
inference time, compared to existing approaches. Our GMNR model generates
3D-aware images of 1024 X 1024 pixels with 17.6 FPS on a single V100. Code :
https://github.com/VIROBO-15/GMNR
