3D-aware Image Generation and Editing with Multi-modal Conditions
Bo Li Yi-ke Li Zhi-fen He Bin Liu Yun-Kun Lai
3D-consistent image generation from a single 2D semantic label is an
important and challenging research topic in computer graphics and computer
vision. Although some related works have made great progress in this field,
most of the existing methods suffer from poor disentanglement performance of
shape and appearance, and lack multi-modal control. In this paper, we propose a
novel end-to-end 3D-aware image generation and editing model incorporating
multiple types of conditional inputs, including pure noise, text and reference
image. On the one hand, we dive into the latent space of 3D Generative
Adversarial Networks (GANs) and propose a novel disentanglement strategy to
separate appearance features from shape features during the generation process.
On the other hand, we propose a unified framework for flexible image generation
and editing tasks with multi-modal conditions. Our method can generate diverse
images with distinct noises, edit the attribute through a text description and
conduct style transfer by giving a reference RGB image. Extensive experiments
demonstrate that the proposed method outperforms alternative approaches both
qualitatively and quantitatively on image generation and editing.