Generative Adversarial Shaders for Real-Time Realism Enhancement
This addresses the need for efficient realism enhancement in applications like embedded and mobile GPUs, offering an incremental improvement over existing methods by reducing runtime and resource requirements.
The paper tackles the problem of realism enhancement in real-time and resource-constrained settings by proposing a generative shader-based approach that achieves temporally stable, faster-than-real-time results with quality competitive with neural network-based methods.
Application of realism enhancement methods, particularly in real-time and resource-constrained settings, has been frustrated by the expense of existing methods. These achieve high quality results only at the cost of long runtimes and high bandwidth, memory, and power requirements. We present an efficient alternative: a high-performance, generative shader-based approach that adapts machine learning techniques to real-time applications, even in resource-constrained settings such as embedded and mobile GPUs. The proposed learnable shader pipeline comprises differentiable functions that can be trained in an end-to-end manner using an adversarial objective, allowing for faithful reproduction of the appearance of a target image set without manual tuning. The shader pipeline is optimized for highly efficient execution on the target device, providing temporally stable, faster-than-real time results with quality competitive with many neural network-based methods.