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2020


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Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

Karimi*, A., von Kügelgen*, J., Schölkopf, B., Valera, I.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020, *equal contribution (conference) Accepted

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arXiv [BibTex]

2020


arXiv [BibTex]


Grasping Field: Learning Implicit Representations for Human Grasps
Grasping Field: Learning Implicit Representations for Human Grasps

Karunratanakul, K., Yang, J., Zhang, Y., Black, M., Muandet, K., Tang, S.

In International Conference on 3D Vision (3DV), November 2020 (inproceedings)

Abstract
Robotic grasping of house-hold objects has made remarkable progress in recent years. Yet, human grasps are still difficult to synthesize realistically. There are several key reasons: (1) the human hand has many degrees of freedom (more than robotic manipulators); (2) the synthesized hand should conform to the surface of the object; and (3) it should interact with the object in a semantically and physically plausible manner. To make progress in this direction, we draw inspiration from the recent progress on learning-based implicit representations for 3D object reconstruction. Specifically, we propose an expressive representation for human grasp modelling that is efficient and easy to integrate with deep neural networks. Our insight is that every point in a three-dimensional space can be characterized by the signed distances to the surface of the hand and the object, respectively. Consequently, the hand, the object, and the contact area can be represented by implicit surfaces in a common space, in which the proximity between the hand and the object can be modelled explicitly. We name this 3D to 2D mapping as Grasping Field, parameterize it with a deep neural network, and learn it from data. We demonstrate that the proposed grasping field is an effective and expressive representation for human grasp generation. Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud. The extensive experiments demonstrate that our generative model compares favorably with a strong baseline and approaches the level of natural human grasps. Furthermore, based on the grasping field representation, we propose a deep network for the challenging task of 3D hand-object interaction reconstruction from a single RGB image. Our method improves the physical plausibility of the hand-object contact reconstruction and achieves comparable performance for 3D hand reconstruction compared to state-of-the-art methods. Our model and code are available for research purpose at https://github.com/korrawe/grasping_field.

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pdf arXiv code [BibTex]


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MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware

Hohmann, M. R., Konieczny, L., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B.

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST), October 2020 (conference) Accepted

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arXiv DOI [BibTex]

arXiv DOI [BibTex]


Label Efficient Visual Abstractions for Autonomous Driving
Label Efficient Visual Abstractions for Autonomous Driving

Behl, A., Chitta, K., Prakash, A., Ohn-Bar, E., Geiger, A.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, October 2020 (conference)

Abstract
It is well known that semantic segmentation can be used as an effective intermediate representation for learning driving policies. However, the task of street scene semantic segmentation requires expensive annotations. Furthermore, segmentation algorithms are often trained irrespective of the actual driving task, using auxiliary image-space loss functions which are not guaranteed to maximize driving metrics such as safety or distance traveled per intervention. In this work, we seek to quantify the impact of reducing segmentation annotation costs on learned behavior cloning agents. We analyze several segmentation-based intermediate representations. We use these visual abstractions to systematically study the trade-off between annotation efficiency and driving performance, ie, the types of classes labeled, the number of image samples used to learn the visual abstraction model, and their granularity (eg, object masks vs. 2D bounding boxes). Our analysis uncovers several practical insights into how segmentation-based visual abstractions can be exploited in a more label efficient manner. Surprisingly, we find that state-of-the-art driving performance can be achieved with orders of magnitude reduction in annotation cost. Beyond label efficiency, we find several additional training benefits when leveraging visual abstractions, such as a significant reduction in the variance of the learned policy when compared to state-of-the-art end-to-end driving models.

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pdf slides video Project Page [BibTex]

pdf slides video Project Page [BibTex]


Convolutional Occupancy Networks
Convolutional Occupancy Networks

Peng, S., Niemeyer, M., Mescheder, L., Pollefeys, M., Geiger, A.

In European Conference on Computer Vision (ECCV), Springer International Publishing, Cham, August 2020 (inproceedings)

Abstract
Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction. While demonstrating promising results, most implicit approaches are limited to comparably simple geometry of single objects and do not scale to more complicated or large-scale scenes. The key limiting factor of implicit methods is their simple fully-connected network architecture which does not allow for integrating local information in the observations or incorporating inductive biases such as translational equivariance. In this paper, we propose Convolutional Occupancy Networks, a more flexible implicit representation for detailed reconstruction of objects and 3D scenes. By combining convolutional encoders with implicit occupancy decoders, our model incorporates inductive biases, enabling structured reasoning in 3D space. We investigate the effectiveness of the proposed representation by reconstructing complex geometry from noisy point clouds and low-resolution voxel representations. We empirically find that our method enables the fine-grained implicit 3D reconstruction of single objects, scales to large indoor scenes, and generalizes well from synthetic to real data.

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pdf suppmat video Project Page [BibTex]

pdf suppmat video Project Page [BibTex]


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Model-Agnostic Counterfactual Explanations for Consequential Decisions

Karimi, A., Barthe, G., Balle, B., Valera, I.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 895-905, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

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arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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More Powerful Selective Kernel Tests for Feature Selection

Lim, J. N., Yamada, M., Jitkrittum, W., Terada, Y., Matsui, S., Shimodaira, H.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 820-830, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

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arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Bayesian Online Prediction of Change Points

Agudelo-España, D., Gomez-Gonzalez, S., Bauer, S., Schölkopf, B., Peters, J.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 320-329, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Semi-supervised learning, causality, and the conditional cluster assumption

von Kügelgen, J., Mey, A., Loog, M., Schölkopf, B.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI) , 124, pages: 1-10, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Kernel Conditional Moment Test via Maximum Moment Restriction

Muandet, K., Jitkrittum, W., Kübler, J. M.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 41-50, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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On the design of consequential ranking algorithms

Tabibian, B., Gómez, V., De, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 171-180, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Importance Sampling via Local Sensitivity

Raj, A., Musco, C., Mackey, L.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 3099-3109, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization

F Alimisis, F., Orvieto, A., Becigneul, G., Lucchi, A.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 1297-1307, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Fair Decisions Despite Imperfect Predictions

Kilbertus, N., Gomez Rodriguez, M., Schölkopf, B., Muandet, K., Valera, I.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 277-287, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Integrals over Gaussians under Linear Domain Constraints

Gessner, A., Kanjilal, O., Hennig, P.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 2764-2774, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Modular Block-diagonal Curvature Approximations for Feedforward Architectures

Dangel, F., Harmeling, S., Hennig, P.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 799-808, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


Category Level Object Pose Estimation via Neural Analysis-by-Synthesis
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis

Chen, X., Dong, Z., Song, J., Geiger, A., Hilliges, O.

In European Conference on Computer Vision (ECCV), Springer International Publishing, Cham, August 2020 (inproceedings)

Abstract
Many object pose estimation algorithms rely on the analysis-by-synthesis framework which requires explicit representations of individual object instances. In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module that is capable of implicitly representing the appearance, shape and pose of entire object categories, thus rendering the need for explicit CAD models per object instance unnecessary. The image synthesis network is designed to efficiently span the pose configuration space so that model capacity can be used to capture the shape and local appearance (i.e., texture) variations jointly. At inference time the synthesized images are compared to the target via an appearance based loss and the error signal is backpropagated through the network to the input parameters. Keeping the network parameters fixed, this allows for iterative optimization of the object pose, shape and appearance in a joint manner and we experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone. When provided with depth measurements, to overcome scale ambiguities, the method can accurately recover the full 6DOF pose successfully.

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Project Page pdf suppmat [BibTex]

Project Page pdf suppmat [BibTex]


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Testing Goodness of Fit of Conditional Density Models with Kernels

Jitkrittum, W., Kanagawa, H., Schölkopf, B.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 221-230, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization

Negiar, G., Dresdner, G., Tsai, A. Y., El Ghaoui, L., Locatello, F., Freund, R. M., Pedregosa, F.

37th International Conference on Machine Learning (ICML), pages: 296-305, July 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Variational Autoencoders with Riemannian Brownian Motion Priors

Kalatzis, D., Eklund, D., Arvanitidis, G., Hauberg, S.

37th International Conference on Machine Learning (ICML), pages: 6789-6799, July 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Variational Bayes in Private Settings (VIPS) (Extended Abstract)

Foulds, J. R., Park, M., Chaudhuri, K., Welling, M.

Proceedings of the 29th International Joint Conference on Artificial Intelligence, (IJCAI-PRICAI), pages: 5050-5054, (Editors: Christian Bessiere), International Joint Conferences on Artificial Intelligence Organization, July 2020, Journal track (conference)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Weakly-Supervised Disentanglement Without Compromises

Locatello, F., Poole, B., Rätsch, G., Schölkopf, B., Bachem, O., Tschannen, M.

37th International Conference on Machine Learning (ICML), pages: 7753-7764, July 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Constant Curvature Graph Convolutional Networks

Bachmann*, G., Becigneul*, G., Ganea, O.

37th International Conference on Machine Learning (ICML), pages: 9118-9128, July 2020, *equal contribution (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks

Kristiadi, A., Hein, M., Hennig, P.

37th International Conference on Machine Learning (ICML), pages: 1226-1236, July 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


Learning of sub-optimal gait controllers for magnetic walking soft millirobots
Learning of sub-optimal gait controllers for magnetic walking soft millirobots

Culha, U., Demir, S. O., Trimpe, S., Sitti, M.

In Proceedings of Robotics: Science and Systems, July 2020, Culha and Demir are equally contributing authors (inproceedings)

Abstract
Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can access confined spaces in the human body. However, due to highly nonlinear soft continuum deformation kinematics, inherent stochastic variability during fabrication at the small scale, and lack of accurate models, the conventional control methods cannot be easily applied. Adaptivity of robot control is additionally crucial for medical operations, as operation environments show large variability, and robot materials may degrade or change over time,which would have deteriorating effects on the robot motion and task performance. Therefore, we propose using a probabilistic learning approach for millimeter-scale magnetic walking soft robots using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme to find controller parameters while optimizing the stride length performance of the walking soft millirobot robot within a small number of physical experiments. We demonstrate adaptation to fabrication variabilities in three different robots and to walking surfaces with different roughness. We also show an improvement in the learning performance by transferring the learning results of one robot to the others as prior information.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Differentiable Likelihoods for Fast Inversion of ‘Likelihood-Free’ Dynamical Systems

Kersting, H., Krämer, N., Schiegg, M., Daniel, C., Tiemann, M., Hennig, P.

37th International Conference on Machine Learning (ICML), pages: 2655-2665, July 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


Actively Learning Gaussian Process Dynamics
Actively Learning Gaussian Process Dynamics

Buisson-Fenet, M., Solowjow, F., Trimpe, S.

2nd Annual Conference on Learning for Dynamics and Control, June 2020 (conference) Accepted

Abstract
Despite the availability of ever more data enabled through modern sensor and computer technology, it still remains an open problem to learn dynamical systems in a sample-efficient way. We propose active learning strategies that leverage information-theoretical properties arising naturally during Gaussian process regression, while respecting constraints on the sampling process imposed by the system dynamics. Sample points are selected in regions with high uncertainty, leading to exploratory behavior and data-efficient training of the model. All results are verified in an extensive numerical benchmark.

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ArXiv [BibTex]

ArXiv [BibTex]


Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes

Geist, A. R., Trimpe, S.

In 2nd Annual Conference on Learning for Dynamics and Control, June 2020 (inproceedings) Accepted

Abstract
The identification of the constrained dynamics of mechanical systems is often challenging. Learning methods promise to ease an analytical analysis, but require considerable amounts of data for training. We propose to combine insights from analytical mechanics with Gaussian process regression to improve the model's data efficiency and constraint integrity. The result is a Gaussian process model that incorporates a priori constraint knowledge such that its predictions adhere to Gauss' principle of least constraint. In return, predictions of the system's acceleration naturally respect potentially non-ideal (non-)holonomic equality constraints. As corollary results, our model enables to infer the acceleration of the unconstrained system from data of the constrained system and enables knowledge transfer between differing constraint configurations.

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Arxiv preprint [BibTex]

Arxiv preprint [BibTex]


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Kernel Conditional Density Operators

Schuster, I., Mollenhauer, M., Klus, S., Muandet, K.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 993-1004, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, June 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control

Zhu, J., Diehl, M., Schölkopf, B.

2nd Annual Conference on Learning for Dynamics and Control (L4DC), 120, pages: 915-923, Proceedings of Machine Learning Research, (Editors: Alexandre M. Bayen and Ali Jadbabaie and George Pappas and Pablo A. Parrilo and Benjamin Recht and Claire Tomlin and Melanie Zeilinger), PMLR, June 2020 (conference)

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arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Disentangling Factors of Variations Using Few Labels

Locatello, F., Tschannen, M., Bauer, S., Rätsch, G., Schölkopf, B., Bachem, O.

8th International Conference on Learning Representations (ICLR), April 2020 (conference)

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arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Mixed-curvature Variational Autoencoders

Skopek, O., Ganea, O., Becigneul, G.

8th International Conference on Learning Representations (ICLR), April 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals
Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals

Laumann, F., von Kügelgen, J., Barahona, M.

ICLR 2020 Workshop "Tackling Climate Change with Machine Learning", April 2020 (conference)

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arXiv PDF [BibTex]

arXiv PDF [BibTex]


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Counterfactuals uncover the modular structure of deep generative models

Besserve, M., Mehrjou, A., Sun, R., Schölkopf, B.

8th International Conference on Learning Representations (ICLR), April 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Towards causal generative scene models via competition of experts
Towards causal generative scene models via competition of experts

von Kügelgen*, J., Ustyuzhaninov*, I., Gehler, P., Bethge, M., Schölkopf, B.

ICLR 2020 Workshop "Causal Learning for Decision Making", April 2020, *equal contribution (conference)

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arXiv PDF [BibTex]

arXiv PDF [BibTex]


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On Mutual Information Maximization for Representation Learning

Tschannen, M., Djolonga, J., Rubenstein, P. K., Gelly, S., Lucic, M.

8th International Conference on Learning Representations (ICLR), April 2020 (conference)

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arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


From Variational to Deterministic Autoencoders
From Variational to Deterministic Autoencoders

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B.

8th International Conference on Learning Representations (ICLR) , April 2020, *equal contribution (conference)

Abstract
Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce “blurry” images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

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arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Radial and Directional Posteriors for Bayesian Deep Learning

Oh, C., Adamczewski, K., Park, M.

Proceedings of the 34th Conference on Artificial Intelligence (AAAI), 34(4):5298-5305, AAAI Press, Febuary 2020, AAAI Technical Track: Machine Learning (conference)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems

Wenk, P., Abbati, G., Osborne, M. A., Schölkopf, B., Krause, A., Bauer, S.

Proceedings of the 34th Conference on Artificial Intelligence (AAAI), 34(4):6364-6371, AAAI Press, Febuary 2020, AAAI Technical Track: Machine Learning (conference)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Interpretable and Differentially Private Predictions

Harder, F., Bauer, M., Park, M.

Proceedings of the 34th Conference on Artificial Intelligence (AAAI), 34(4):4083-4090, AAAI Press, Febuary 2020, AAAI Technical Track: Machine Learning (conference)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Commentary on the Unsupervised Learning of Disentangled Representations

Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B., Bachem, O.

Proceedings of the 34th Conference on Artificial Intelligence (AAAI), 34(9):13681-13684, AAAI Press, Febuary 2020, Sister Conference Track (conference)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image

Paschalidou, D., Gool, L., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
Humans perceive the 3D world as a set of distinct objects that are characterized by various low-level (geometry, reflectance) and high-level (connectivity, adjacency, symmetry) properties. Recent methods based on convolutional neural networks (CNNs) demonstrated impressive progress in 3D reconstruction, even when using a single 2D image as input. However, the majority of these methods focuses on recovering the local 3D geometry of an object without considering its part-based decomposition or relations between parts. We address this challenging problem by proposing a novel formulation that allows to jointly recover the geometry of a 3D object as a set of primitives as well as their latent hierarchical structure without part-level supervision. Our model recovers the higher level structural decomposition of various objects in the form of a binary tree of primitives, where simple parts are represented with fewer primitives and more complex parts are modeled with more components. Our experiments on the ShapeNet and D-FAUST datasets demonstrate that considering the organization of parts indeed facilitates reasoning about 3D geometry.

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pdf suppmat Video 2 Project Page Slides Poster Video 1 [BibTex]

pdf suppmat Video 2 Project Page Slides Poster Video 1 [BibTex]


Towards 5-DoF Control of an Untethered Magnetic Millirobot via MRI Gradient Coils
Towards 5-DoF Control of an Untethered Magnetic Millirobot via MRI Gradient Coils

Onder Erin, D. A. M. E. T., Sitti, M.

In IEEE International Conference on Robotics and Automation (ICRA), 2020 (inproceedings)

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[BibTex]

[BibTex]


GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis

Schwarz, K., Liao, Y., Niemeyer, M., Geiger, A.

In Advances in Neural Information Processing Systems (NeurIPS), 2020 (inproceedings)

Abstract
While 2D generative adversarial networks have enabled high-resolution image synthesis, they largely lack an understanding of the 3D world and the image formation process. Thus, they do not provide precise control over camera viewpoint or object pose. To address this problem, several recent approaches leverage intermediate voxel-based representations in combination with differentiable rendering. However, existing methods either produce low image resolution or fall short in disentangling camera and scene properties, eg, the object identity may vary with the viewpoint. In this paper, we propose a generative model for radiance fields which have recently proven successful for novel view synthesis of a single scene. In contrast to voxel-based representations, radiance fields are not confined to a coarse discretization of the 3D space, yet allow for disentangling camera and scene properties while degrading gracefully in the presence of reconstruction ambiguity. By introducing a multi-scale patch-based discriminator, we demonstrate synthesis of high-resolution images while training our model from unposed 2D images alone. We systematically analyze our approach on several challenging synthetic and real-world datasets. Our experiments reveal that radiance fields are a powerful representation for generative image synthesis, leading to 3D consistent models that render with high fidelity.

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[BibTex]

[BibTex]


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A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models

Agudelo-España, D., Zadaianchuk, A., Wenk, P., Garg, A., Akpo, J., Grimminger, F., Viereck, J., Naveau, M., Righetti, L., Martius, G., Krause, A., Schölkopf, B., Bauer, S., Wüthrich, M.

IEEE International Conference on Robotics and Automation (ICRA), 2020 (conference) Accepted

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Project Page PDF [BibTex]

Project Page PDF [BibTex]


Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis

Liao, Y., Schwarz, K., Mescheder, L., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are learned directly from images. However, current image synthesis models operate in the 2D domain where disentangling 3D properties such as camera viewpoint or object pose is challenging. Furthermore, they lack an interpretable and controllable representation. Our key hypothesis is that the image generation process should be modeled in 3D space as the physical world surrounding us is intrinsically three-dimensional. We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain. We demonstrate that our model is able to disentangle latent 3D factors of simple multi-object scenes in an unsupervised fashion from raw images. Compared to pure 2D baselines, it allows for synthesizing scenes that are consistent wrt. changes in viewpoint or object pose. We further evaluate various 3D representations in terms of their usefulness for this challenging task.

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pdf suppmat Video 2 Project Page Video 1 Slides Poster [BibTex]

pdf suppmat Video 2 Project Page Video 1 Slides Poster [BibTex]


Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem

Zhu, J., Jitkrittum, W., Diehl, M., Schölkopf, B.

In 59th IEEE Conference on Decision and Control (CDC), 2020 (inproceedings) Accepted

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[BibTex]

[BibTex]