April 22 - Munich AI, ML and Computer Vision Meetup
Join the Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.
Date, Time and Location
Apr 22, 2026
5:30 - 8:30 PM
Impact Hub Munich
Gotzinger Str. 8
München, Germany
Learning Disentangled Motion Representations for Open-World Motion Transfer
Recent progress in image- and text-to-video generation has made it possible to synthesize visually compelling videos, yet these models typically lack an explicit, reusable notion of motion. In this talk, I will present recent work on learning high-level, content-independent motion representations directly from open-world video data, with a focus on our NeurIPS spotlight paper introducing DisMo.
By disentangling motion semantics from appearance and object identity, such representations enable open-world motion transfer across semantically unrelated entities and provide a flexible interface for adapting and fine-tuning modern video generation models. Beyond generation, I will discuss how abstract motion representations support downstream motion understanding tasks and why they offer a promising direction for more controllable, general, and future-proof video models. The talk will conclude with a broader perspective on the opportunities and challenges of motion-centric representations in computer vision and video learning.
About the Speaker
Thomas Ressler-Antal is a PhD student at the Computer Vision & Learning Lab at LMU Munich, advised by Björn Ommer. My research focuses on representation learning from large-scale, open-world video data, with an emphasis on disentangling motion from appearance. I am particularly interested in motion understanding, video generation, and transferable representations that enable controllable and general-purpose video models. My work has
OBS! Vi reserverar oss för eventuella felskrivningar i informationen som vi ger om det här evenemanget. Besök evenemangets hemsida för att säkerställa exempelvis datum, öppettider, priser och plats.
