activity
20242026
most citedInsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion Architecture

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Schrödinger's Cat: Probabilistic Representation and Prediction of Potential Scene Kinematics

Timy Phan, Jannik Wiese, Björn Ommer

Predicting how a scene may evolve from partial observations requires reasoning about multiple possible futures rather than committing to a single trajectory. Existing approaches ei…

cs.CV2026

Probabilistic Precipitation Nowcasting with Rectified Flow Transformers

Johannes Schusterbauer, Jannik Wiese, Nick Stracke +2

Accurate weather forecasts are essential across various domains and are safety-critical in extreme weather conditions. Compared to simulation-based forecasting, data-driven approac…

cs.CV2026

Envisioning the Future, One Step at a Time

Stefan Andreas Baumann, Jannik Wiese, Tommaso Martorella +2

Accurately anticipating how complex, diverse scenes will evolve requires models that represent uncertainty, simulate along extended interaction chains, and efficiently explore many…

cs.LG2025

Masked Conditioning for Deep Generative Models

Phillip Mueller, Jannik Wiese, Sebastian Mueller +1

Datasets in engineering domains are often small, sparsely labeled, and contain numerical as well as categorical conditions. Additionally. computational resources are typically limi…

cs.CV2024★ 1 cited

InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion Architecture

Phillip Mueller, Jannik Wiese, Ioan Craciun +1

Recent advancements in image synthesis are fueled by the advent of large-scale diffusion models. Yet, integrating realistic object visualizations seamlessly into new or existing ba…