论文标题
部分可观测时空混沌系统的无模型预测
VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relighting
论文作者
论文摘要
我们提出了Volux-Gan，这是一个生成框架，可以使3D感知面孔具有令人信服的重新构成。我们的主要贡献是一种体积的HDRI重新处理方法，该方法可以有效地沿每个3D射线在任何所需的HDR环境图中有效地积累反照率，弥漫性和镜面照明贡献。此外，我们展示了使用多个歧视器监督图像分解过程的重要性。特别是，我们提出了一种数据增强技术，该技术利用了单个图像肖像重新确定的最新进展来执行一致的几何形状，反照率，弥漫性和镜头组件。多个实验和与其他生成框架的比较表明，我们的模型是朝着迈向光真逼真的可靠3D生成模型的一步。
We propose VoLux-GAN, a generative framework to synthesize 3D-aware faces with convincing relighting. Our main contribution is a volumetric HDRI relighting method that can efficiently accumulate albedo, diffuse and specular lighting contributions along each 3D ray for any desired HDR environmental map. Additionally, we show the importance of supervising the image decomposition process using multiple discriminators. In particular, we propose a data augmentation technique that leverages recent advances in single image portrait relighting to enforce consistent geometry, albedo, diffuse and specular components. Multiple experiments and comparisons with other generative frameworks show how our model is a step forward towards photorealistic relightable 3D generative models.
