5 code implementations • CVPR 2018 • Matthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich Müller
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.
Ranked #3 on Node Classification on Cora
1 code implementation • NeurIPS 2018 • Jan Eric Lenssen, Matthias Fey, Pascal Libuschewski
We present group equivariant capsule networks, a framework to introduce guaranteed equivariance and invariance properties to the capsule network idea.
1 code implementation • 4 Oct 2018 • Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe
We show that GNNs have the same expressiveness as the $1$-WL in terms of distinguishing non-isomorphic (sub-)graphs.
Ranked #4 on Graph Classification on NCI1
4 code implementations • 6 Mar 2019 • Matthias Fey, Jan Eric Lenssen
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
Ranked #4 on Graph Classification on REDDIT-B
2 code implementations • CVPR 2020 • Jan Eric Lenssen, Christian Osendorfer, Jonathan Masci
This results in a state-of-the-art surface normal estimator that is robust to noise, outliers and point density variation, preserves sharp features through anisotropic kernels and equivariance through a local quaternion-based spatial transformer.
Ranked #7 on Surface Normals Estimation on PCPNet
2 code implementations • ECCV 2020 • Yongheng Zhao, Tolga Birdal, Jan Eric Lenssen, Emanuele Menegatti, Leonidas Guibas, Federico Tombari
We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations, as well as invariant to permutations of the input points.
1 code implementation • ECCV 2020 • Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, Richard Newcombe
Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception.
no code implementations • 16 May 2022 • Keyang Zhou, Bharat Lal Bhatnagar, Jan Eric Lenssen, Gerard Pons-Moll
The core of our method are TOCH fields, a novel spatio-temporal representation for modeling correspondences between hands and objects during interaction.
1 code implementation • 27 Jul 2022 • Garvita Tiwari, Dimitrije Antic, Jan Eric Lenssen, Nikolaos Sarafianos, Tony Tung, Gerard Pons-Moll
The resulting high-dimensional implicit function can be differentiated with respect to the input poses and thus can be used to project arbitrary poses onto the manifold by using gradient descent on the set of 3-dimensional hyperspheres.
no code implementations • ICCV 2023 • Christopher Wewer, Eddy Ilg, Bernt Schiele, Jan Eric Lenssen
(1) We design the first neural point representation on a category level by utilizing the concept of coherent point clouds.
no code implementations • 2 Dec 2023 • Devikalyan Das, Christopher Wewer, Raza Yunus, Eddy Ilg, Jan Eric Lenssen
However, owing to the ill-posed nature of this problem, there has been no solution that can provide consistent, high-quality novel views from camera positions that are significantly different from the training views.
no code implementations • 7 Dec 2023 • Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec
The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links.
no code implementations • 12 Dec 2023 • Xianghui Xie, Bharat Lal Bhatnagar, Jan Eric Lenssen, Gerard Pons-Moll
We generate 1M+ human-object interaction pairs in 3D and leverage this large-scale data to train our HDM (Hierarchical Diffusion Model), a novel method to reconstruct interacting human and unseen objects, without any templates.
1 code implementation • 21 Dec 2023 • Philipp Schröppel, Christopher Wewer, Jan Eric Lenssen, Eddy Ilg, Thomas Brox
However, none of the existing models enable disentangled generation to control the shape and appearance separately.
no code implementations • 5 Mar 2024 • Yannan He, Garvita Tiwari, Tolga Birdal, Jan Eric Lenssen, Gerard Pons-Moll
Faithfully modeling the space of articulations is a crucial task that allows recovery and generation of realistic poses, and remains a notorious challenge.
no code implementations • 22 Mar 2024 • Raza Yunus, Jan Eric Lenssen, Michael Niemeyer, Yiyi Liao, Christian Rupprecht, Christian Theobalt, Gerard Pons-Moll, Jia-Bin Huang, Vladislav Golyanik, Eddy Ilg
Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision.
no code implementations • 24 Mar 2024 • Christopher Wewer, Kevin Raj, Eddy Ilg, Bernt Schiele, Jan Eric Lenssen
We present latentSplat, a method to predict semantic Gaussians in a 3D latent space that can be splatted and decoded by a light-weight generative 2D architecture.
no code implementations • 31 Mar 2024 • Jialin Chen, Jan Eric Lenssen, Aosong Feng, Weihua Hu, Matthias Fey, Leandros Tassiulas, Jure Leskovec, Rex Ying
Motivated by our observation of a correlation between the time series model's performance boost against channel mixing and the intrinsic similarity on a pair of channels, we developed a novel and adaptable Channel Clustering Module (CCM).
no code implementations • 2 Apr 2024 • Keyang Zhou, Bharat Lal Bhatnagar, Jan Eric Lenssen, Gerard Pons-Moll
Generating realistic hand motion sequences in interaction with objects has gained increasing attention with the growing interest in digital humans.