Despite the growing success of 3D-aware GANs, which can be trained on 2D
images to generate high-quality 3D assets, they still rely on multi-view images
with camera annotations to synthesize sufficient details from all viewing
directions. However, the scarce availability of calibrated multi-view image
datasets, especially in comparison to single-view images, has limited the
potential of 3D GANs. Moreover, while bypassing camera pose annotations with a
camera distribution constraint reduces dependence on exact camera parameters,
it still struggles to generate a consistent orientation of 3D assets. To this
end, we propose SYM3D, a novel 3D-aware GAN designed to leverage the prevalent
reflectional symmetry structure found in natural and man-made objects,
alongside a proposed view-aware spatial attention mechanism in learning the 3D
representation. We evaluate SYM3D on both synthetic (ShapeNet Chairs, Cars, and
Airplanes) and real-world datasets (ABO-Chair), demonstrating its superior
performance in capturing detailed geometry and texture, even when trained on
only single-view images. Finally, we demonstrate the effectiveness of
incorporating symmetry regularization in helping reduce artifacts in the
modeling of 3D assets in the text-to-3D task.

提出了 SYM3D，一种新颖的 3D-aware GAN，通过利用自然和人造物体中普遍存在的反射对称结构，结合提出的视角感知空间注意机制来学习三维表示，从而在只使用单视图图像进行训练时捕捉详细的几何和纹理，同时证明了在文本到三维任务中利用对称性正则化有助于减少模型中的伪影。