@proceedings {7044,
	title = {Behavior-Driven Synthesis of Human Dynamics},
	year = {2021},
	url = {https://arxiv.org/abs/2103.04677},
	author = {Andreas Blattmann and Timo Milbich and Michael Dorkenwald and Bj{\"o}rn Ommer}
}
@conference {7068,
	title = {iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis},
	booktitle = {Proceedings of the International Conference on Computer Vision (ICCV)},
	year = {2021},
	url = {https://arxiv.org/abs/2107.02790},
	author = {Andreas Blattmann and Timo Milbich and Michael Dorkenwald and Bj{\"o}rn Ommer}
}
@proceedings {7053,
	title = {Stochastic Image-to-Video Synthesis usin cINNs},
	year = {2021},
	author = {Michael Dorkenwald and Timo Milbich and Andreas Blattmann and Robin Rombach and Konstantinos G. Derpanis and Bj{\"o}rn Ommer}
}
@proceedings {7063,
	title = {Understanding Object Dynamics for Interactive Image-to-Video Synthesis},
	year = {2021},
	abstract = {What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative model learns to infer natural object dynamics as a response to user interaction and learns about the interrelations between different object body regions. Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time. In contrast to existing work on video prediction, we do not synthesize arbitrary realistic videos but enable local interactive control of the deformation. Our model is not restricted to particular object categories and can transfer dynamics onto novel unseen object instances. Extensive experiments on diverse objects demonstrate the effectiveness of our approach compared to common video prediction frameworks.},
	url = {https://arxiv.org/abs/2106.11303v1},
	author = {Andreas Blattmann and Timo Milbich and Michael Dorkenwald and Bj{\"o}rn Ommer}
}
@article {7045,
	title = {Unsupervised behaviour analysis and magnification (uBAM) using deep learning},
	journal = {Nature Machine Intelligence},
	year = {2021},
	abstract = {Motor behaviour analysis is essential to biomedical research and clinical diagnostics as it provides a non-invasive strategy for identifying motor impairment and its change caused by interventions. State-of-the-art instrumented movement analysis is time- and cost-intensive, because it requires the placement of physical or virtual markers. As well as the effort required for marking the keypoints or annotations necessary for training or fine-tuning a detector, users need to know the interesting behaviour beforehand to provide meaningful keypoints. Here, we introduce unsupervised behaviour analysis and magnification (uBAM), an automatic deep learning algorithm for analysing behaviour by discovering and magnifying deviations. A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement. Besides discovering and quantifying deviations in behaviour, we also propose a generative model for visually magnifying subtle behaviour differences directly in a video without requiring a detour via keypoints or annotations. Essential for this magnification of deviations, even across different individuals, is a disentangling of appearance and behaviour. Evaluations on rodents and human patients with neurological diseases demonstrate the wide applicability of our approach. Moreover, combining optogenetic stimulation with our unsupervised behaviour analysis shows its suitability as a non-invasive diagnostic tool correlating function to brain plasticity.},
	doi = {https://doi.org/10.1038/s42256-021-00326-x},
	url = {https://rdcu.be/ch6pL},
	author = {Biagio Brattoli and Uta B{\"u}chler and Michael Dorkenwald and Philipp Reiser and Linard Filli and Fritjof Helmchen and Anna-Sophia Wahl and Bj{\"o}rn Ommer}
}
@proceedings {7042,
	title = {Unsupervised Magnification of Posture Deviations Across Subjects},
	year = {2020},
	author = {Michael Dorkenwald and Uta B{\"u}chler and Bj{\"o}rn Ommer}
}
