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Frank Fundel

3 accepted papers

2025

CleanDIFT: Diffusion Features without Noise

CVPR 2025poster

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 these features generally need to add noise to images before passing them through the model to obtain the semantic featu…

2025

Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment

CVPR 2025poster

Diffusion models have revolutionized generative tasks through high-fidelity outputs, yet flow matching (FM) offers faster inference and empirical performance gains. However, current foundation FM models are computationally prohibitive for finetuning, while diffusion models like Stable Diffusion bene…

2025

DisMo: Disentangled Motion Representations for Open-World Motion Transfer

NeurIPS 2025spotlight

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 initial frames. However, these models often fail to provide an explicit representation of motion separate from content, limi…

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