52 citations · 74 across the 7 of their papers we have counts for
12 papers · 1 filter
Towards Unified Keyframe Propagation Models
Patrick Esser, Peter Michael, Soumyadip Sengupta
Many video editing tasks such as rotoscoping or object removal require the propagation of context across frames. While transformers and other attention-based approaches that aggreg…
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
Patrick Esser, Robin Rombach, Andreas Blattmann +1
Autoregressive models and their sequential factorization of the data likelihood have recently demonstrated great potential for image representation and synthesis. Nevertheless, the…
Geometry-Free View Synthesis: Transformers and no 3D Priors
Robin Rombach, Patrick Esser, Björn Ommer
Is a geometric model required to synthesize novel views from a single image? Being bound to local convolutions, CNNs need explicit 3D biases to model geometric transformations. In…
Shape or Texture: Understanding Discriminative Features in CNNs
Md Amirul Islam, Matthew Kowal, Patrick Esser +4
Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs act…
A Note on Data Biases in Generative Models
Patrick Esser, Robin Rombach, Björn Ommer
It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter a…
Taming Transformers for High-Resolution Image Synthesis
Patrick Esser, Robin Rombach, Björn Ommer
Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain n…