6 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.CV2025
MaskFlow: Discrete Flows For Flexible and Efficient Long Video Generation
Michael Fuest, Vincent Tao Hu, Björn Ommer
Generating long, high-quality videos remains a challenge due to the complex interplay of spatial and temporal dynamics and hardware limitations. In this work, we introduce MaskFlow…
cs.LG2025★ 1 cited
CENTS: Generating synthetic electricity consumption time series for rare and unseen scenarios
Michael Fuest, Alfredo Cuesta, Kalyan Veeramachaneni
Recent breakthroughs in large-scale generative modeling have demonstrated the potential of foundation models in domains such as natural language, computer vision, and protein struc…
cs.CV2024★ 6 cited
Diffusion Models and Representation Learning: A Survey
Michael Fuest, Pingchuan Ma, Ming Gui +3
Diffusion Models are popular generative modeling methods in various vision tasks, attracting significant attention. They can be considered a unique instance of self-supervised lear…