4 papers
DisMo: Disentangled Motion Representations for Open-World Motion Transfer
Thomas Ressler-Antal, Frank Fundel, Malek Ben Alaya +4
Recent advances in text-to-video (T2V) and image-to-video (I2V) models, have enabled the creation of visually compelling and dynamic videos from simple textual descriptions or init…
Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment
Johannes Schusterbauer, Ming Gui, Frank Fundel +1
Diffusion models have revolutionized generative tasks through high-fidelity outputs, yet flow matching (FM) offers faster inference and empirical performance gains. However, curren…
Distillation of Diffusion Features for Semantic Correspondence
Frank Fundel, Johannes Schusterbauer, Vincent Tao Hu +1
Semantic correspondence, the task of determining relationships between different parts of images, underpins various applications including 3D reconstruction, image-to-image transla…
CleanDIFT: Diffusion Features without Noise
Nick Stracke, Stefan Andreas Baumann, Kolja Bauer +2
Internal features from large-scale pre-trained diffusion models have recently been established as powerful semantic descriptors for a wide range of downstream tasks. Works that use…