1 code implementation • 26 Jul 2022 • Robin Rombach, Andreas Blattmann, Björn Ommer
In RDMs, a set of nearest neighbors is retrieved from an external database during training for each training instance, and the diffusion model is conditioned on these informative samples.
2 code implementations • 25 Apr 2022 • Andreas Blattmann, Robin Rombach, Kaan Oktay, Jonas Müller, Björn Ommer
Much of this success is due to the scalability of these architectures and hence caused by a dramatic increase in model complexity and in the computational resources invested in training these models.
13 code implementations • CVPR 2022 • Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond.
Ranked #4 on
Image Generation
on ImageNet 512x512
no code implementations • NeurIPS 2021 • Patrick Esser, Robin Rombach, Andreas Blattmann, Björn Ommer
Thus, in contrast to pure autoregressive models, it can solve free-form image inpainting and, in the case of conditional models, local, text-guided image modification without requiring mask-specific training.
2 code implementations • ICCV 2021 • Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer
There will be distinctive movement, despite evident variations caused by the stochastic nature of our world.
1 code implementation • CVPR 2021 • Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer
Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time.
1 code implementation • CVPR 2021 • Michael Dorkenwald, Timo Milbich, Andreas Blattmann, Robin Rombach, Konstantinos G. Derpanis, Björn Ommer
Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame.
1 code implementation • CVPR 2021 • Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer
Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence.