@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}
}
@proceedings {7004,
	title = {Unsupervised Part Discovery by Unsupervised Disentanglement},
	year = {2020},
	month = {09/2020},
	address = {T{\"u}bingen},
	abstract = {We address the problem of discovering part segmentations of articulated objects without supervision. In contrast to keypoints, part segmentations provide information about part localizations on the level of individual pixels. Capturing both locations and semantics, they are an attractive target for supervised learning approaches. However, large annotation costs limit the scalability of supervised algorithms to other object categories than humans. Unsupervised approaches potentially allow to use much more data at a lower cost. Most existing unsupervised approaches focus on learning abstract representations to be refined with supervision into the final representation. Our approach leverages a generative model consisting of two disentangled representations for an object{\textquoteright}s shape and appearance and a latent variable for the part segmentation. From a single image, the trained model infers a semantic part segmentation map. In experiments, we compare our approach to previous state-of-the-art approaches and observe significant gains in segmentation accuracy and shape consistency. Our work demonstrates the feasibility to discover semantic part segmentations without supervision.},
	url = {https://compvis.github.io/unsupervised-part-segmentation/},
	author = {Sandro Braun and Patrick Esser and Bj{\"o}rn Ommer}
}
@article {6339,
	title = {Unsupervised Representation Learning by Discovering Reliable Image Relations},
	journal = {Pattern Recognition},
	volume = {102},
	year = {2020},
	month = {June 2020},
	url = {http://arxiv.org/abs/1911.07808},
	author = {Timo Milbich and Omair Ghori and Bj{\"o}rn Ommer}
}
@conference {6301,
	title = {Unsupervised Part-Based Disentangling of Object Shape and Appearance},
	booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Oral + Best paper finalist: top 45 / 5160 submissions)},
	year = {2019},
	author = {Dominik Lorenz and Leonard Bereska and Timo Milbich and Bj{\"o}rn Ommer}
}
@conference {6323,
	title = {Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis},
	booktitle = {Proceedings of the Intl. Conf. on Computer Vision (ICCV)},
	year = {2019},
	abstract = {Deep generative models come with the promise to learn an explainable representation for visual objects that allows image sampling, synthesis, and selective modification. The main challenge is to learn to properly model the independent latent characteristics of an object, especially its appearance and pose. We present a novel approach that learns disentangled representations of these characteristics and explains them individually. Training requires only pairs of images depicting the same object appearance, but no pose annotations. We propose an additional classifier that estimates the minimal amount of regularization required to enforce disentanglement. Thus both representations together can completely explain an image while being independent of each other. Previous methods based on adversarial approaches fail to enforce this independence, while methods based on variational approaches lead to uninformative representations. In experiments on diverse object categories, the approach successfully recombines pose and appearance to reconstruct and retarget novel synthesized images. We achieve significant improvements over state-of-the-art methods which utilize the same level of supervision, and reach performances comparable to those of pose-supervised approaches. However, we can handle the vast body of articulated object classes for which no pose models/annotations are available.},
	url = {https://compvis.github.io/robust-disentangling/},
	author = {Patrick Esser and Johannes Haux and Bj{\"o}rn Ommer}
}
@proceedings {6300,
	title = {Using a Transformation Content Block For Image Style Transfer},
	year = {2019},
	author = {Dmytro Kotovenko and Sanakoyeu, A. and Sabine Lang and Ma, P. and Bj{\"o}rn Ommer}
}
@conference {6187,
	title = {Unsupervised Video Understanding by Reconciliation of Posture Similarities},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
	year = {2017},
	url = {https://hciweb.iwr.uni-heidelberg.de/compvis/research/tmilbich_iccv17},
	author = {Timo Milbich and Miguel Bautista and Ekaterina Sutter and Bj{\"o}rn Ommer}
}
