6 citations · 6 across the 8 of their papers we have counts for
4 papers · 2 filters
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…
Adapting Self-Supervised Representations as a Latent Space for Efficient Generation
Ming Gui, Johannes Schusterbauer, Timy Phan +4
We introduce Representation Tokenizer (RepTok), a generative modeling framework that represents an image using a single continuous latent token obtained from self-supervised vision…
SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models
Pingchuan Ma, Xiaopei Yang, Yusong Li +4
Explicitly disentangling style and content in vision models remains challenging due to their semantic overlap and the subjectivity of human perception. Existing methods propose sep…
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…