Zobrazeno 1 - 10
of 1 300
pro vyhledávání: '"A. Ommer"'
Autor:
Fuest, Michael, Ma, Pingchuan, Gui, Ming, Fischer, Johannes S., Hu, Vincent Tao, Ommer, Bjorn
Diffusion Models are popular generative modeling methods in various vision tasks, attracting significant attention. They can be considered a unique instance of self-supervised learning methods due to their independence from label annotation. This sur
Externí odkaz:
http://arxiv.org/abs/2407.00783
Controllable text-to-image (T2I) diffusion models have shown impressive performance in generating high-quality visual content through the incorporation of various conditions. Current methods, however, exhibit limited performance when guided by skelet
Externí odkaz:
http://arxiv.org/abs/2406.02485
Autor:
Stracke, Nick, Baumann, Stefan Andreas, Susskind, Joshua M., Bautista, Miguel Angel, Ommer, Björn
Text-to-image generative models have become a prominent and powerful tool that excels at generating high-resolution realistic images. However, guiding the generative process of these models to consider detailed forms of conditioning reflecting style
Externí odkaz:
http://arxiv.org/abs/2405.07913
Autor:
Baumann, Stefan Andreas, Krause, Felix, Neumayr, Michael, Stracke, Nick, Hu, Vincent Tao, Ommer, Björn
In recent years, advances in text-to-image (T2I) diffusion models have substantially elevated the quality of their generated images. However, achieving fine-grained control over attributes remains a challenge due to the limitations of natural languag
Externí odkaz:
http://arxiv.org/abs/2403.17064
In this work we propose a novel method for unsupervised controllable video generation. Once trained on a dataset of unannotated videos, at inference our model is capable of both composing scenes of predefined object parts and animating them in a plau
Externí odkaz:
http://arxiv.org/abs/2403.14368
Autor:
Hu, Vincent Tao, Baumann, Stefan Andreas, Gui, Ming, Grebenkova, Olga, Ma, Pingchuan, Fischer, Johannes, Ommer, Björn
The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to e
Externí odkaz:
http://arxiv.org/abs/2403.13802
Autor:
Gui, Ming, Fischer, Johannes S., Prestel, Ulrich, Ma, Pingchuan, Kotovenko, Dmytro, Grebenkova, Olga, Baumann, Stefan Andreas, Hu, Vincent Tao, Ommer, Björn
Monocular depth estimation is crucial for numerous downstream vision tasks and applications. Current discriminative approaches to this problem are limited due to blurry artifacts, while state-of-the-art generative methods suffer from slow sampling du
Externí odkaz:
http://arxiv.org/abs/2403.13788
Autor:
Manduchi, Laura, Pandey, Kushagra, Bamler, Robert, Cotterell, Ryan, Däubener, Sina, Fellenz, Sophie, Fischer, Asja, Gärtner, Thomas, Kirchler, Matthias, Kloft, Marius, Li, Yingzhen, Lippert, Christoph, de Melo, Gerard, Nalisnick, Eric, Ommer, Björn, Ranganath, Rajesh, Rudolph, Maja, Ullrich, Karen, Broeck, Guy Van den, Vogt, Julia E, Wang, Yixin, Wenzel, Florian, Wood, Frank, Mandt, Stephan, Fortuin, Vincent
The field of deep generative modeling has grown rapidly and consistently over the years. With the availability of massive amounts of training data coupled with advances in scalable unsupervised learning paradigms, recent large-scale generative models
Externí odkaz:
http://arxiv.org/abs/2403.00025
Long lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography acquisitions without severe deterioration of image quality. To this end, numerous reconstruction and noise reduc
Externí odkaz:
http://arxiv.org/abs/2401.04661
Autor:
Hu, Vincent Tao, Yin, Wenzhe, Ma, Pingchuan, Chen, Yunlu, Fernando, Basura, Asano, Yuki M, Gavves, Efstratios, Mettes, Pascal, Ommer, Bjorn, Snoek, Cees G. M.
Human motion synthesis is a fundamental task in computer animation. Recent methods based on diffusion models or GPT structure demonstrate commendable performance but exhibit drawbacks in terms of slow sampling speeds and error accumulation. In this p
Externí odkaz:
http://arxiv.org/abs/2312.08895