33 citations · 44 across the 22 of their papers we have counts for
6 papers · 1 filter
Image Generation Diversity Issues and How to Tame Them
Mischa Dombrowski, Weitong Zhang, Sarah Cechnicka +2
Generative methods now produce outputs nearly indistinguishable from real data but often fail to fully capture the data distribution. Unlike quality issues, diversity limitations i…
Uncovering Hidden Subspaces in Video Diffusion Models Using Re-Identification
Mischa Dombrowski, Hadrien Reynaud, Bernhard Kainz
Latent Video Diffusion Models can easily deceive casual observers and domain experts alike thanks to the produced image quality and temporal consistency. Beyond entertainment, this…
Noise Crystallization and Liquid Noise: Zero-shot Video Generation using Image Diffusion Models
Muhammad Haaris Khan, Hadrien Reynaud, Bernhard Kainz
Although powerful for image generation, consistent and controllable video is a longstanding problem for diffusion models. Video models require extensive training and computational…
JVID: Joint Video-Image Diffusion for Visual-Quality and Temporal-Consistency in Video Generation
Hadrien Reynaud, Matthew Baugh, Mischa Dombrowski +3
We introduce the Joint Video-Image Diffusion model (JVID), a novel approach to generating high-quality and temporally coherent videos. We achieve this by integrating two diffusion…
URCDM: Ultra-Resolution Image Synthesis in Histopathology
Sarah Cechnicka, James Ball, Matthew Baugh +6
Diagnosing medical conditions from histopathology data requires a thorough analysis across the various resolutions of Whole Slide Images (WSI). However, existing generative methods…
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing
Hadrien Reynaud, Qingjie Meng, Mischa Dombrowski +5
To make medical datasets accessible without sharing sensitive patient information, we introduce a novel end-to-end approach for generative de-identification of dynamic medical imag…