no code implementations • 27 May 2024 • Maria Korosteleva, Timur Levent Kesdogan, Fabian Kemper, Stephan Wenninger, Jasmin Koller, Yuhan Zhang, Mario Botsch, Olga Sorkine-Hornung
Recent research interest in the learning-based processing of garments, from virtual fitting to generation and reconstruction, stumbles on a scarcity of high-quality public data in the domain.
no code implementations • 31 Oct 2023 • Qingqing Zhao, Peizhuo Li, Wang Yifan, Olga Sorkine-Hornung, Gordon Wetzstein
Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist-created poses to noisy poses estimated directly from images.
no code implementations • 9 Jun 2023 • Alexandre Binninger, Amir Hertz, Olga Sorkine-Hornung, Daniel Cohen-Or, Raja Giryes
We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches, including those of abstract nature.
1 code implementation • 1 Jun 2023 • Weiyu Li, Xuelin Chen, Peizhuo Li, Olga Sorkine-Hornung, Baoquan Chen
At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches.
1 code implementation • 6 Feb 2023 • Floor Verhoeven, Tanguy Magne, Olga Sorkine-Hornung
In this paper we propose a novel method for grid-based single-image document unwarping.
1 code implementation • 5 Oct 2022 • Robin Magnet, Jing Ren, Olga Sorkine-Hornung, Maks Ovsjanikov
We introduce pointwise map smoothness via the Dirichlet energy into the functional map pipeline, and propose an algorithm for optimizing it efficiently, which leads to high-quality results in challenging settings.
1 code implementation • CVPR 2023 • Sigal Raab, Inbal Leibovitch, Peizhuo Li, Kfir Aberman, Olga Sorkine-Hornung, Daniel Cohen-Or
In this work, we present MoDi -- a generative model trained in an unsupervised setting from an extremely diverse, unstructured and unlabeled dataset.
1 code implementation • 5 May 2022 • Peizhuo Li, Kfir Aberman, Zihan Zhang, Rana Hanocka, Olga Sorkine-Hornung
We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence.
1 code implementation • 31 Jan 2022 • Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, Daniel Cohen-Or
Neural implicit fields are quickly emerging as an attractive representation for learning based techniques.
no code implementations • 11 Oct 2021 • Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, Daniel Cohen-Or
The method drapes the source mesh over the target geometry and at the same time seeks to preserve the carefully designed characteristics of the source mesh.
1 code implementation • ICLR 2022 • Wang Yifan, Lukas Rahmann, Olga Sorkine-Hornung
We present implicit displacement fields, a novel representation for detailed 3D geometry.
1 code implementation • 6 May 2021 • Peizhuo Li, Kfir Aberman, Rana Hanocka, Libin Liu, Olga Sorkine-Hornung, Baoquan Chen
Furthermore, we propose neural blend shapes--a set of corrective pose-dependent shapes which improve the deformation quality in the joint regions in order to address the notorious artifacts resulting from standard rigging and skinning.
1 code implementation • NeurIPS 2021 • Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, Daniel Cohen-Or
Multilayer-perceptrons (MLP) are known to struggle with learning functions of high-frequencies, and in particular cases with wide frequency bands.
no code implementations • CVPR 2021 • Wang Yifan, Shihao Wu, Cengiz Oztireli, Olga Sorkine-Hornung
Neural implicit functions have emerged as a powerful representation for surfaces in 3D.
1 code implementation • 12 May 2020 • Kfir Aberman, Peizhuo Li, Dani Lischinski, Olga Sorkine-Hornung, Daniel Cohen-Or, Baoquan Chen
In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons.
1 code implementation • CVPR 2020 • Wang Yifan, Noam Aigerman, Vladimir G. Kim, Siddhartha Chaudhuri, Olga Sorkine-Hornung
The goal of our method is to warp a source shape to match the general structure of a target shape, while preserving the surface details of the source.
1 code implementation • 10 Jun 2019 • Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, Olga Sorkine-Hornung
We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds.
3 code implementations • CVPR 2019 • Wang Yifan, Shihao Wu, Hui Huang, Daniel Cohen-Or, Olga Sorkine-Hornung
We present a detail-driven deep neural network for point set upsampling.
6 code implementations • 9 Apr 2018 • Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers
Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality.
Ranked #14 on Image Super-Resolution on BSD100 - 4x upscaling