Learning 3D-Aware GANs from Unposed Images with Template Feature Field

arXiv:2404.05705v1 Announce Type: new
Abstract: Collecting accurate camera poses of training images has been shown to well serve the learning of 3D-aware generative adversarial networks (GANs) yet can be quite expensive in practice. This work targets learning 3D-aware GANs from unposed images, for which we propose to perform on-the-fly pose estimation of training images with a learned template feature field (TeFF). Concretely, in addition to a generative radiance field as in previous approaches, we ask the generator to also learn …

abstract adversarial arxiv cs.cv feature fly gans generative generative adversarial networks images networks practice serve targets template training type work