no code implementations • 30 Apr 2024 • Paul Engstler, Andrea Vedaldi, Iro Laina, Christian Rupprecht
These works often depend on pre-trained monocular depth estimators to lift the generated images into 3D, fusing them with the existing scene representation.
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 • 22 Mar 2024 • Ruining Li, Chuanxia Zheng, Christian Rupprecht, Andrea Vedaldi
We introduce DragAPart, a method that, given an image and a set of drags as input, can generate a new image of the same object in a new state, compatible with the action of the drags.
no code implementations • 13 Feb 2024 • Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, Filippos Kokkinos
A mitigation is to fine-tune the 2D generator to be multi-view aware, which can help distillation or can be combined with reconstruction networks to output 3D objects directly.
no code implementations • 4 Jan 2024 • Zizhang Li, Dor Litvak, Ruining Li, Yunzhi Zhang, Tomas Jakab, Christian Rupprecht, Shangzhe Wu, Andrea Vedaldi, Jiajun Wu
We show that prior category-specific attempts fail to generalize to rare species with limited training images.
1 code implementation • 20 Dec 2023 • Stanislaw Szymanowicz, Christian Rupprecht, Andrea Vedaldi
Splatter Image is based on Gaussian Splatting, which allows fast and high-quality reconstruction of 3D scenes from multiple images.
no code implementations • 19 Dec 2023 • Jinghao Zhou, Tomas Jakab, Philip Torr, Christian Rupprecht
Recently, 3D generative models have made impressive progress, enabling the generation of almost arbitrary 3D assets from text or image inputs.
no code implementations • 7 Dec 2023 • Jianyuan Wang, Nikita Karaev, Christian Rupprecht, David Novotny
Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer.
no code implementations • 6 Dec 2023 • Felix Wimbauer, Bichen Wu, Edgar Schoenfeld, Xiaoliang Dai, Ji Hou, Zijian He, Artsiom Sanakoyeu, Peizhao Zhang, Sam Tsai, Jonas Kohler, Christian Rupprecht, Daniel Cremers, Peter Vajda, Jialiang Wang
However, one of the major drawbacks of diffusion models is that the image generation process is costly.
no code implementations • 24 Nov 2023 • Paul Engstler, Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina
Self-supervised learning (SSL) can be used to solve complex visual tasks without human labels.
Ranked #2 on Unsupervised Instance Segmentation on COCO val2017
no code implementations • 11 Oct 2023 • Adrian Hayler, Felix Wimbauer, Dominik Muhle, Christian Rupprecht, Daniel Cremers
3D semantic scene understanding is a fundamental challenge in computer vision.
1 code implementation • 7 Oct 2023 • Nina Shvetsova, Anna Kukleva, Xudong Hong, Christian Rupprecht, Bernt Schiele, Hilde Kuehne
Specifically, we prompt an LLM to create plausible video descriptions based on ASR narrations of the video for a large-scale instructional video dataset.
1 code implementation • 14 Jul 2023 • Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, Christian Rupprecht
We introduce CoTracker, a transformer-based model that tracks dense points in a frame jointly across a video sequence.
no code implementations • ICCV 2023 • Jianyuan Wang, Christian Rupprecht, David Novotny
Camera pose estimation is a long-standing computer vision problem that to date often relies on classical methods, such as handcrafted keypoint matching, RANSAC and bundle adjustment.
no code implementations • 15 Jun 2023 • Laurynas Karazija, Iro Laina, Andrea Vedaldi, Christian Rupprecht
This provides a distribution of appearances for a given text circumventing the ambiguity problem.
no code implementations • ICCV 2023 • Stanislaw Szymanowicz, Christian Rupprecht, Andrea Vedaldi
We fit a diffusion model to a large number of viewsets for a given category of objects.
1 code implementation • CVPR 2023 • Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, Christian Rupprecht
The network learns to pool information from neighboring frames to improve the temporal consistency of its predictions.
no code implementations • 20 Apr 2023 • Tomas Jakab, Ruining Li, Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi
We propose a framework that uses an image generator, such as Stable Diffusion, to generate synthetic training data that are sufficiently clean and do not require further manual curation, enabling the learning of such a reconstruction network from scratch.
no code implementations • ICCV 2023 • Aleksandar Shtedritski, Christian Rupprecht, Andrea Vedaldi
Large-scale Vision-Language Models, such as CLIP, learn powerful image-text representations that have found numerous applications, from zero-shot classification to text-to-image generation.
no code implementations • CVPR 2023 • Yaoyao Liu, Bernt Schiele, Andrea Vedaldi, Christian Rupprecht
Incremental object detection (IOD) aims to train an object detector in phases, each with annotations for new object categories.
Class-Incremental Object Detection Knowledge Distillation +3
1 code implementation • 23 Mar 2023 • Anna Kukleva, Moritz Böhle, Bernt Schiele, Hilde Kuehne, Christian Rupprecht
Such a schedule results in a constant `task switching' between an emphasis on instance discrimination and group-wise discrimination and thereby ensures that the model learns both group-wise features, as well as instance-specific details.
3 code implementations • 21 Feb 2023 • Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi
We consider the problem of reconstructing a full 360{\deg} photographic model of an object from a single image of it.
2 code implementations • 21 Feb 2023 • Luke Melas-Kyriazi, Christian Rupprecht, Andrea Vedaldi
Reconstructing the 3D shape of an object from a single RGB image is a long-standing and highly challenging problem in computer vision.
1 code implementation • CVPR 2023 • Felix Wimbauer, Nan Yang, Christian Rupprecht, Daniel Cremers
By directly sampling color from the available views instead of storing color in the density field, our scene representation becomes significantly less complex compared to NeRFs, and a neural network can predict it in a single forward pass.
no code implementations • CVPR 2023 • Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Andrea Vedaldi
We consider the problem of reconstructing a full 360deg photographic model of an object from a single image of it.
no code implementations • CVPR 2023 • Luke Melas-Kyriazi, Christian Rupprecht, Andrea Vedaldi
Reconstructing the 3D shape of an object from a single RGB image is a long-standing problem in computer vision.
no code implementations • CVPR 2023 • Shangzhe Wu, Ruining Li, Tomas Jakab, Christian Rupprecht, Andrea Vedaldi
We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and lighting of an articulated animal like a horse given a single test image as input.
no code implementations • 8 Nov 2022 • Julien Lecompagnon, Philipp Daniel Hirsch, Christian Rupprecht, Mathias Ziegler
In this work, we present a novel approach to photothermal super resolution based thermographic resolution of internal defects using two-dimensional pixel pattern-based active photothermal laser heating in conjunction with subsequent numerical reconstruction to achieve a high-resolution reconstruction of internal defect structures.
no code implementations • 21 Oct 2022 • Laurynas Karazija, Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi
We propose a new approach to learn to segment multiple image objects without manual supervision.
1 code implementation • 19 Oct 2022 • Laura Hanu, James Thewlis, Yuki M. Asano, Christian Rupprecht
In this paper, we a) introduce a new dataset of videos, titles and comments; b) present an attention-based mechanism that allows the model to learn from sometimes irrelevant data such as comments; c) show that by using comments, our method is able to learn better, more contextualised, representations for image, video and audio representations.
1 code implementation • CVPR 2022 • Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi
We find that these eigenvectors already decompose an image into meaningful segments, and can be readily used to localize objects in a scene.
no code implementations • 16 May 2022 • Subhabrata Choudhury, Laurynas Karazija, Iro Laina, Andrea Vedaldi, Christian Rupprecht
Motion, measured via optical flow, provides a powerful cue to discover and learn objects in images and videos.
Ranked #4 on Unsupervised Object Segmentation on SegTrack-v2
no code implementations • 1 Mar 2022 • Julien Lecompagnon, Samim Ahmadi, Philipp Hirsch, Christian Rupprecht, Mathias Ziegler
Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures.
1 code implementation • CVPR 2022 • Felix Wimbauer, Shangzhe Wu, Christian Rupprecht
With increasing focus on augmented and virtual reality applications (XR) comes the demand for algorithms that can lift objects from images and videos into representations that are suitable for a wide variety of related 3D tasks.
1 code implementation • 19 Nov 2021 • Laurynas Karazija, Iro Laina, Christian Rupprecht
We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data.
Ranked #3 on Unsupervised Object Segmentation on ClevrTex
1 code implementation • NeurIPS 2021 • Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi
First, we construct a proxy task through a set of objectives that encourages the model to learn a meaningful decomposition of the image into its parts.
Ranked #1 on Unsupervised Keypoint Estimation on CUB
1 code implementation • 5 Nov 2021 • Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi
We then train a fine-grained textual similarity model that matches image descriptions with documents on a sentence-level basis.
no code implementations • 29 Sep 2021 • Laura Hanu, Yuki M Asano, James Thewlis, Christian Rupprecht
Learning strong representations for multi-modal retrieval is an important problem for many applications, such as recommendation and search.
1 code implementation • NeurIPS Workshop ImageNet_PPF 2021 • Yuki M. Asano, Christian Rupprecht, Andrew Zisserman, Andrea Vedaldi
On the other hand, state-of-the-art pretraining is nowadays obtained with unsupervised methods, meaning that labelled datasets such as ImageNet may not be necessary, or perhaps not even optimal, for model pretraining.
no code implementations • 22 Jul 2021 • Shangzhe Wu, Tomas Jakab, Christian Rupprecht, Andrea Vedaldi
In this paper, we present DOVE, a method that learns textured 3D models of deformable object categories from monocular videos available online, without keypoint, viewpoint or template shape supervision.
1 code implementation • ICLR 2022 • Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi
Recent research has shown that numerous human-interpretable directions exist in the latent space of GANs.
2 code implementations • CVPR 2021 • Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab
Is critical input information encoded in specific sparse pathways within the neural network?
no code implementations • 1 Jan 2021 • Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab
Is critical input information encoded in specific sparse paths within the network?
1 code implementation • NeurIPS 2020 • Yuki M. Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi
A large part of the current success of deep learning lies in the effectiveness of data -- more precisely: labelled data.
1 code implementation • CVPR 2020 • Helisa Dhamo, Azade Farshad, Iro Laina, Nassir Navab, Gregory D. Hager, Federico Tombari, Christian Rupprecht
In our work, we address the novel problem of image manipulation from scene graphs, in which a user can edit images by merely applying changes in the nodes or edges of a semantic graph that is generated from the image.
no code implementations • 25 Nov 2019 • Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab
Attributing the output of a neural network to the contribution of given input elements is a way of shedding light on the black-box nature of neural networks.
1 code implementation • CVPR 2020 • Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi
We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision.
4 code implementations • ICLR 2020 • Yuki Markus Asano, Christian Rupprecht, Andrea Vedaldi
Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks.
Ranked #8 on Contrastive Learning on imagenet-1k
1 code implementation • 27 Oct 2019 • Henry Martin, Ye Hong, Dominik Bucher, Christian Rupprecht, René Buffat
The goal of the IARAI competition traffic4cast was to predict the city-wide traffic status within a 15-minute time window, based on information from the previous hour.
no code implementations • ICCV 2019 • Iro Laina, Christian Rupprecht, Nassir Navab
The core component of our approach is a shared latent space that is structured by visual concepts.
no code implementations • 4 Jun 2019 • Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi
Specifically, given a single image of the object seen from an arbitrary viewpoint, our model predicts a symmetric canonical view, the corresponding 3D shape and a viewpoint transformation, and trains with the goal of reconstructing the input view, resembling an auto-encoder.
2 code implementations • ICLR 2020 • Yuki M. Asano, Christian Rupprecht, Andrea Vedaldi
We look critically at popular self-supervision techniques for learning deep convolutional neural networks without manual labels.
no code implementations • ICLR 2020 • Christian Rupprecht, Cyril Ibrahim, Christopher J. Pal
Further, critical states in which a very high or a very low reward can be achieved are often interesting to understand the situational awareness of the system as they can correspond to risky states.
no code implementations • ICCV 2019 • Fabian Manhardt, Diego Martin Arroyo, Christian Rupprecht, Benjamin Busam, Tolga Birdal, Nassir Navab, Federico Tombari
For each object instance we predict multiple pose and class outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures.
no code implementations • 2 Nov 2018 • Ghazal Ghazaei, Iro Laina, Christian Rupprecht, Federico Tombari, Nassir Navab, Kianoush Nazarpour
Further, we reformulate the problem of robotic grasping by replacing conventional grasp rectangles with grasp belief maps, which hold more precise location information than a rectangle and account for the uncertainty inherent to the task.
no code implementations • 20 Apr 2018 • Maximilian Baust, Florian Ludwig, Christian Rupprecht, Matthias Kohl, Stefan Braunewell
Variational methods for revealing visual concepts learned by convolutional neural networks have gained significant attention during the last years.
no code implementations • CVPR 2018 • Christian Rupprecht, Iro Laina, Nassir Navab, Gregory D. Hager, Federico Tombari
Interaction and collaboration between humans and intelligent machines has become increasingly important as machine learning methods move into real-world applications that involve end users.
no code implementations • ICLR 2018 • Sanjeev Kumar, Christian Rupprecht, Federico Tombari, Gregory D. Hager
We introduce a new approach to estimate continuous actions using actor-critic algorithms for reinforcement learning problems.
no code implementations • 8 Nov 2017 • Chris Paxton, Kapil Katyal, Christian Rupprecht, Raman Arora, Gregory D. Hager
Ideally, we would combine the ability of machine learning to leverage big data for learning the semantics of a task, while using techniques from task planning to reliably generalize to new environment.
no code implementations • 14 Jun 2017 • Christian Rupprecht, Ansh Kapil, Nan Liu, Lamberto Ballan, Federico Tombari
One of the main problems in webly-supervised learning is cleaning the noisy labeled data from the web.
no code implementations • 30 Mar 2017 • Iro Laina, Nicola Rieke, Christian Rupprecht, Josué Page Vizcaíno, Abouzar Eslami, Federico Tombari, Nassir Navab
Real-time instrument tracking is a crucial requirement for various computer-assisted interventions.
no code implementations • ICLR 2018 • Robert DiPietro, Christian Rupprecht, Nassir Navab, Gregory D. Hager
Recurrent neural networks (RNNs) have achieved state-of-the-art performance on many diverse tasks, from machine translation to surgical activity recognition, yet training RNNs to capture long-term dependencies remains difficult.
no code implementations • ICCV 2017 • Christian Rupprecht, Iro Laina, Robert DiPietro, Maximilian Baust, Federico Tombari, Nassir Navab, Gregory D. Hager
In future prediction, for example, many distinct outcomes are equally valid.
no code implementations • 20 Sep 2016 • Florian Dubost, Loic Peter, Christian Rupprecht, Benjamin Gutierrez-Becker, Nassir Navab
We propose a novel hands-free method to interactively segment 3D medical volumes.
no code implementations • 18 Jul 2016 • Christian Rupprecht, Elizabeth Huaroc, Maximilian Baust, Nassir Navab
We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework.
no code implementations • 24 Jun 2016 • Felix Grün, Christian Rupprecht, Nassir Navab, Federico Tombari
Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous surge in performance.
18 code implementations • 1 Jun 2016 • Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, Nassir Navab
This paper addresses the problem of estimating the depth map of a scene given a single RGB image.
no code implementations • 23 Oct 2015 • Kanishka Sharma, Loic Peter, Christian Rupprecht, Anna Caroli, Lichao Wang, Andrea Remuzzi, Maximilian Baust, Nassir Navab
This paper presents a method for 3D segmentation of kidneys from patients with autosomal dominant polycystic kidney disease (ADPKD) and severe renal insufficiency, using computed tomography (CT) data.
no code implementations • CVPR 2015 • Christian Rupprecht, Loic Peter, Nassir Navab
Consider the following scenario between a human user and the computer.
1 code implementation • ICCV 2015 • Vasileios Belagiannis, Christian Rupprecht, Gustavo Carneiro, Nassir Navab
Convolutional Neural Networks (ConvNets) have successfully contributed to improve the accuracy of regression-based methods for computer vision tasks such as human pose estimation, landmark localization, and object detection.