GIRAFFE HD: A High-Resolution 3D-aware Generative Model
Yang Xue, Yuheng Li, Krishna Kumar Singh, Yong Jae Lee · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022
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 [38] 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 (5122resolution 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.