147 citations · 355 across the 24 of their papers we have counts for
14 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…
ATISS: Autoregressive Transformers for Indoor Scene Synthesis
Despoina Paschalidou, Amlan Kar, Maria Shugrina +3
The ability to synthesize realistic and diverse indoor furniture layouts automatically or based on partial input, unlocks many applications, from better interactive 3D tools to dat…
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…
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation
Niklas Hanselmann, Nick Schneider, Benedikt Ortelt +1
In order to handle the challenges of autonomous driving, deep learning has proven to be crucial in tackling increasingly complex tasks, such as 3D detection or instance segmentatio…
Shape As Points: A Differentiable Poisson Solver
Songyou Peng, Chiyu "Max" Jiang, Yiyi Liao +3
In recent years, neural implicit representations gained popularity in 3D reconstruction due to their expressiveness and flexibility. However, the implicit nature of neural implicit…