Monte-Carlo path tracing is a powerful technique for realistic image
synthesis but suffers from high levels of noise at low sample counts, limiting
its use in real-time applications. To address this, we propose a framework with
end-to-end training of a sampling importance network, a latent space encoder
network, and a denoiser network. Our approach uses reinforcement learning to
optimize the sampling importance network, thus avoiding explicit numerically
approximated gradients. Our method does not aggregate the sampled values per
pixel by averaging but keeps all sampled values which are then fed into the
latent space encoder. The encoder replaces handcrafted spatiotemporal
heuristics by learned representations in a latent space. Finally, a neural
denoiser is trained to refine the output image. Our approach increases visual
quality on several challenging datasets and reduces rendering times for equal
quality by a factor of 1.6x compared to the previous state-of-the-art, making
it a promising solution for real-time applications.

通过强化学习优化重要性采样网络，将采样的值输入潜在空间编码器，最终训练一个神经去噪器，该方法在多个数据集上提高了视觉质量，使渲染时间比现有技术提高了 1.6 倍，成为实时应用的有希望的解决方案。

基于强化学习的状态感知神经自适应采样与去噪方法，用于实时路径追踪

RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time  Path Tracing

Recent advances in generative adversarial networks (GANs) have shown great
potentials in realistic image synthesis whereas most existing works address
synthesis realism in either appearance space or geometry space but few in both.
This paper presents an innovative Spatial Fusion GAN (SF-GAN) that combines a
geometry synthesizer and an appearance synthesizer to achieve synthesis realism
in both geometry and appearance spaces. The geometry synthesizer learns
contextual geometries of background images and transforms and places foreground
objects into the background images unanimously. The appearance synthesizer
adjusts the color, brightness and styles of the foreground objects and embeds
them into background images harmoniously, where a guided filter is introduced
for detail preserving. The two synthesizers are inter-connected as mutual
references which can be trained end-to-end without supervision. The SF-GAN has
been evaluated in two tasks: (1) realistic scene text image synthesis for
training better recognition models; (2) glass and hat wearing for realistic
matching glasses and hats with real portraits. Qualitative and quantitative
comparisons with the state-of-the-art demonstrate the superiority of the
proposed SF-GAN.

该论文提出了一种创新的空间融合 GAN（SF-GAN），它结合了几何合成器和外观合成器，以在几何和外观空间中实现综合的逼真性。两个综合器彼此相互连接，可以进行端到端训练，无需监督。SF-GAN 已在两个任务中进行了评估，定性和定量比较证明了该方法的优越性。

图像生成的空间融合生成对抗网络

Spatial Fusion GAN for Image Synthesis

Generative adversarial networks (GANs) while being very versatile in
realistic image synthesis, still are sensitive to the input distribution. Given
a set of data that has an imbalance in the distribution, the networks are
susceptible to missing modes and not capturing the data distribution. While
various methods have been tried to improve training of GANs, these have not
addressed the challenges of covering the full data distribution. Specifically,
a generator is not penalized for missing a mode. We show that these are
therefore still susceptible to not capturing the full data distribution.
In this paper, we propose a simple approach that combines an encoder based
objective with novel loss functions for generator and discriminator that
improves the solution in terms of capturing missing modes. We validate that the
proposed method results in substantial improvements through its detailed
analysis on toy and real datasets. The quantitative and qualitative results
demonstrate that the proposed method improves the solution for the problem of
missing modes and improves training of GANs.

本文提出了一种新的简单方法，将基于编码器的目标函数与新颖的损失函数相结合，以在丢失模式问题上改进生成对抗网络（GAN）的训练，其中详细分析了玩具和真实数据集的量化和定性结果，证明了该方法在解决缺失模式问题和提高 GAN 训练方面的有效性。