321 citations · 407 across the 10 of their papers we have counts for
10 papers
ReRoPE: Repurposing RoPE for Relative Camera Control
Chunyang Li, Yuanbo Yang, Jiahao Shao +3
Video generation with controllable camera viewpoints is essential for applications such as interactive content creation, gaming, and simulation. Existing methods typically adapt pr…
WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space
Katja Schwarz, Seung Wook Kim, Jun Gao +3
Modern learning-based approaches to 3D-aware image synthesis achieve high photorealism and 3D-consistent viewpoint changes for the generated images. Existing approaches represent i…
NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
Seung Wook Kim, Bradley Brown, Kangxue Yin +6
Automatically generating high-quality real world 3D scenes is of enormous interest for applications such as virtual reality and robotics simulation. Towards this goal, we introduce…
ARAH: Animatable Volume Rendering of Articulated Human SDFs
Shaofei Wang, Katja Schwarz, Andreas Geiger +1
Combining human body models with differentiable rendering has recently enabled animatable avatars of clothed humans from sparse sets of multi-view RGB videos. While state-of-the-ar…
VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids
Katja Schwarz, Axel Sauer, Michael Niemeyer +2
State-of-the-art 3D-aware generative models rely on coordinate-based MLPs to parameterize 3D radiance fields. While demonstrating impressive results, querying an MLP for every samp…
StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
Axel Sauer, Katja Schwarz, Andreas Geiger
Computer graphics has experienced a recent surge of data-centric approaches for photorealistic and controllable content creation. StyleGAN in particular sets new standards for gene…
On the Frequency Bias of Generative Models
Katja Schwarz, Yiyi Liao, Andreas Geiger
The key objective of Generative Adversarial Networks (GANs) is to generate new data with the same statistics as the provided training data. However, multiple recent works show that…
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…
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
Yiyi Liao, Katja Schwarz, Lars Mescheder +1
In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical renderi…
Multi-Scale Convolutions for Learning Context Aware Feature Representations
Nikolai Ufer, Kam To Lui, Katja Schwarz +2
Finding semantic correspondences is a challenging problem. With the breakthrough of CNNs stronger features are available for tasks like classification but not specifically for the…