Andreas Geiger

Publications of Katja Schwarz

GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
K. Schwarz, Y. Liao, M. Niemeyer and A. Geiger
Advances in Neural Information Processing Systems (NeurIPS), 2020
Abstract: While 2D generative adversarial networks have enabled high-resolution image synthesis, they largely lack an understanding of the 3D world and the image formation process. Thus, they do not provide precise control over camera viewpoint or object pose. To address this problem, several recent approaches leverage intermediate voxel-based representations in combination with differentiable rendering. However, existing methods either produce low image resolution or fall short in disentangling camera and scene properties, eg, the object identity may vary with the viewpoint. In this paper, we propose a generative model for radiance fields which have recently proven successful for novel view synthesis of a single scene. In contrast to voxel-based representations, radiance fields are not confined to a coarse discretization of the 3D space, yet allow for disentangling camera and scene properties while degrading gracefully in the presence of reconstruction ambiguity. By introducing a multi-scale patch-based discriminator, we demonstrate synthesis of high-resolution images while training our model from unposed 2D images alone. We systematically analyze our approach on several challenging synthetic and real-world datasets. Our experiments reveal that radiance fields are a powerful representation for generative image synthesis, leading to 3D consistent models that render with high fidelity.
Latex Bibtex Citation:
  author = {Katja Schwarz and Yiyi Liao and Michael Niemeyer and Andreas Geiger},
  title = {GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020}
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
Y. Liao, K. Schwarz, L. Mescheder and A. Geiger
Conference on Computer Vision and Pattern Recognition (CVPR), 2020
Abstract: In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are learned directly from images. However, current image synthesis models operate in the 2D domain where disentangling 3D properties such as camera viewpoint or object pose is challenging. Furthermore, they lack an interpretable and controllable representation. Our key hypothesis is that the image generation process should be modeled in 3D space as the physical world surrounding us is intrinsically three-dimensional. We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain. We demonstrate that our model is able to disentangle latent 3D factors of simple multi-object scenes in an unsupervised fashion from raw images. Compared to pure 2D baselines, it allows for synthesizing scenes that are consistent wrt. changes in viewpoint or object pose. We further evaluate various 3D representations in terms of their usefulness for this challenging task.
Latex Bibtex Citation:
  author = {Yiyi Liao and Katja Schwarz and Lars Mescheder and Andreas Geiger},
  title = {Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020}

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