论文标题
长颈鹿高清：高分辨率3D感知生成模型
GIRAFFE HD: A High-Resolution 3D-aware Generative Model
论文作者
论文摘要
3D感知的生成模型表明，引入3D信息可以导致更具控制的图像生成。特别是，当前的最新模型长颈鹿可以控制每个对象的旋转，翻译，比例和场景摄像头姿势，而无需相应的监督。但是，只有在图像分辨率较低时，长颈鹿才能很好地工作。我们提出了长颈鹿HD，这是一种高分辨率3D感知的生成模型，该模型继承了长颈鹿的所有可控功能，同时生成高质量的高分辨率图像（$ 512^2 $分辨率及以上）。关键的想法是利用基于样式的神经渲染器，并独立生成前景和背景来迫使它们的分离，同时施加一致性约束，以将它们缝合在一起，以复合连贯的最终图像。我们在多个自然图像数据集上演示了最新的3D可控高分辨率图像生成。
3D-aware generative models have shown that the introduction of 3D information can lead to more controllable image generation. In particular, the current state-of-the-art model GIRAFFE can control each object's rotation, translation, scale, and scene camera pose without corresponding supervision. However, GIRAFFE only operates well when the image resolution is low. We propose GIRAFFE HD, a high-resolution 3D-aware generative model that inherits all of GIRAFFE's controllable features while generating high-quality, high-resolution images ($512^2$ resolution and above). The key idea is to leverage a style-based neural renderer, and to independently generate the foreground and background to force their disentanglement while imposing consistency constraints to stitch them together to composite a coherent final image. We demonstrate state-of-the-art 3D controllable high-resolution image generation on multiple natural image datasets.
