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.

翻译：真实感增强方法的应用，尤其是在实时和资源受限的环境中，一直受到现有方法高昂成本的制约。这些方法虽然能够实现高质量结果，但代价是运行时间长、带宽、内存和功耗要求高。我们提出一种高效的替代方案：一种基于高性能生成式着色器的方法，将机器学习技术适配到实时应用中，甚至在嵌入式及移动GPU等资源受限的场景下也能适用。所提出的可学习着色器管线包含可微函数，能够利用对抗目标进行端到端训练，从而无需手动调参即可忠实再现目标图像集的外观。该着色器管线针对目标设备上的高效执行进行了优化，能够提供时间稳定的、超实时速度的结果，其质量可与许多基于神经网络的方法相媲美。