  In this paper, we leverage advances in neural networks towards forming a
neural rendering for controllable image generation, and thereby bypassing the
need for detailed modeling in conventional graphics pipeline. To this end, we
present Neural Graphics Pipeline (NGP), a hybrid generative model that brings
together neural and traditional image formation models. NGP decomposes the
image into a set of interpretable appearance feature maps, uncovering direct
control handles for controllable image generation. To form an image, NGP
generates coarse 3D models that are fed into neural rendering modules to
produce view-specific interpretable 2D maps, which are then composited into the
final output image using a traditional image formation model. Our approach
offers control over image generation by providing direct handles controlling
illumination and camera parameters, in addition to control over shape and
appearance variations. The key challenge is to learn these controls through
unsupervised training that links generated coarse 3D models with unpaired real
images via neural and traditional (e.g., Blinn- Phong) rendering functions,
without establishing an explicit correspondence between them. We demonstrate
the effectiveness of our approach on controllable image generation of
single-object scenes. We evaluate our hybrid modeling framework, compare with
neural-only generation methods (namely, DCGAN, LSGAN, WGAN-GP, VON, and SRNs),
report improvement in FID scores against real images, and demonstrate that NGP
supports direct controls common in traditional forward rendering. Code is
available at http://geometry.cs.ucl.ac.uk/projects/2021/ngp.
