20 papers
Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation
Johannes Schusterbauer, Ming Gui, Yusong Li +3
Diffusion- and flow-based models usually allocate compute uniformly across space, updating all patches with the same timestep and number of function evaluations. While convenient,…
Learning Long-term Motion Embeddings for Efficient Kinematics Generation
Nick Stracke, Kolja Bauer, Stefan Andreas Baumann +3
Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scene dynamics, exploring multip…
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…
FlowTouch: View-Invariant Visuo-Tactile Prediction
Seongjin Bien, Carlo Kneissl, Tobias Jülg +6
Tactile sensation is essential for contact-rich manipulation tasks. It provides direct feedback on object geometry, surface properties, and interaction forces, enhancing perception…
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…
What If : Understanding Motion Through Sparse Interactions
Stefan Andreas Baumann, Nick Stracke, Timy Phan +1
Understanding the dynamics of a physical scene involves reasoning about the diverse ways it can potentially change, especially as a result of local interactions. We present the Flo…