10.9k citations
- Max Planck SocietyDE155 papers
- Heidelberg UniversityDE52 papers
- Centre National de la Recherche ScientifiqueFR46 papers
- Jagiellonian UniversityPL44 papers
- Charles UniversityCZ40 papers
- Polish Academy of SciencesPL40 papers
- Humboldt-Universität zu BerlinDE39 papers
- European Organization for Nuclear ResearchCH38 papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR37 papers
- University of BirminghamGB37 papers
- University of OxfordGB36 papers
- Rutherford Appleton LaboratoryGB35 papers
11 papers · 1 filter
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…
Projected GANs Converge Faster
Axel Sauer, Kashyap Chitta, Jens Müller +1
Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train. They need careful regularization, vast amounts of compute, and expensive hyper-para…
Neural Relightable Participating Media Rendering
Quan Zheng, Gurprit Singh, Hans-Peter Seidel
Learning neural radiance fields of a scene has recently allowed realistic novel view synthesis of the scene, but they are limited to synthesize images under the original fixed ligh…
Learning Realistic Human Reposing using Cyclic Self-Supervision with 3D Shape, Pose, and Appearance Consistency
Soubhik Sanyal, Alex Vorobiov, Timo Bolkart +5
Synthesizing images of a person in novel poses from a single image is a highly ambiguous task. Most existing approaches require paired training images; i.e. images of the same pers…
Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection
Farzaneh Rezaeianaran, Rakshith Shetty, Rahaf Aljundi +3
In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate…
NEAT: Neural Attention Fields for End-to-End Autonomous Driving
Kashyap Chitta, Aditya Prakash, Andreas Geiger
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a no…