  Neural materials typically consist of a collection of neural features along
with a decoder network. The main challenge in integrating such models in
real-time rendering pipelines lies in the large size required to store their
features in GPU memory and the complexity of evaluating the network
efficiently. We present a neural material model whose features and decoder are
specifically designed to be used in real-time rendering pipelines. Our
framework leverages hardware-based block compression (BC) texture formats to
store the learned features and trains the model to output the material
information continuously in space and scale. To achieve this, we organize the
features in a block-based manner and emulate BC6 decompression during training,
making it possible to export them as regular BC6 textures. This structure
allows us to use high resolution features while maintaining a low memory
footprint. Consequently, this enhances our model's overall capability, enabling
the use of a lightweight and simple decoder architecture that can be evaluated
directly in a shader. Furthermore, since the learned features can be decoded
continuously, it allows for random uv sampling and smooth transition between
scales without needing any subsequent filtering. As a result, our neural
material has a small memory footprint, can be decoded extremely fast adding a
minimal computational overhead to the rendering pipeline.

Computer Vision and Pattern Recognition;Graphics