321 citations · 565 across the 12 of their papers we have counts for
4 papers · 1 filter
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
Michael Niemeyer, Andreas Geiger
Deep generative models allow for photorealistic image synthesis at high resolutions. But for many applications, this is not enough: content creation also needs to be controllable.…
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer +1
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, the…
Learning Implicit Surface Light Fields
Michael Oechsle, Michael Niemeyer, Lars Mescheder +2
Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to repres…
Convolutional Occupancy Networks
Songyou Peng, Michael Niemeyer, Lars Mescheder +2
Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction. While demonstrating promising results, most implicit approaches are limited t…