289 citations · 409 across the 12 of their papers we have counts for
14 papers
CASCADE: A Spatio-Temporal-Causal Reasoning Representation and Dataset for Driving
Jenny Schmalfuss, Despoina Paschalidou, Simon Gerstenecker +2
Reasoning is a promising route to the generalization that autonomous driving requires in the long tail, as it can infer how the elements of a scene depend on one another and traver…
SPEAR: A Simulator for Photorealistic Embodied AI Research
Mike Roberts, Renhan Wang, Rushikesh Zawar +10
Interactive simulators have become powerful tools for training embodied agents and generating synthetic visual data, but existing photorealistic simulators suffer from limited gene…
Generating Physically Realistic and Directable Human Motions from Multi-Modal Inputs
Aayam Shrestha, Pan Liu, German Ros +2
This work focuses on generating realistic, physically-based human behaviors from multi-modal inputs, which may only partially specify the desired motion. For example, the input may…
Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relighting
Benjamin Ummenhofer, Sanskar Agrawal, Rene Sepulveda +6
Reconstructing an object from photos and placing it virtually in a new environment goes beyond the standard novel view synthesis task as the appearance of the object has to not onl…
All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes
Jose L. Gómez, Manuel Silva, Antonio Seoane +5
We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios. Developed using high-quality geometry and materials,…
SPIGAN: Privileged Adversarial Learning from Simulation
Kuan-Hui Lee, German Ros, Jie Li +1
Deep Learning for Computer Vision depends mainly on the source of supervision.Photo-realistic simulators can generate large-scale automatically labeled syntheticdata, but introduce…
Joint Coarse-And-Fine Reasoning for Deep Optical Flow
Victor Vaquero, German Ros, Francesc Moreno-Noguer +2
We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine re…
Physical Representation-based Predicate Optimization for a Visual Analytics Database
Michael R. Anderson, Michael Cafarella, German Ros +1
Querying the content of images, video, and other non-textual data sources requires expensive content extraction methods. Modern extraction techniques are based on deep convolutiona…
A Dataset To Evaluate The Representations Learned By Video Prediction Models
Ryan Szeto, Simon Stent, German Ros +1
We present a parameterized synthetic dataset called Moving Symbols to support the objective study of video prediction networks. Using several instantiations of the dataset in which…
CARLA: An Open Urban Driving Simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla +2
We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autono…
From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example
Antonio M. Lopez, Jiaolong Xu, Jose L. Gomez +2
Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing…
Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation
German Ros, Simon Stent, Pablo F. Alcantarilla +1
In this work we investigate the problem of road scene semantic segmentation using Deconvolutional Networks (DNs). Several constraints limit the practical performance of DNs in this…
Fast and Robust Fixed-Rank Matrix Recovery
German Ros, Julio Guerrero
We address the problem of efficient sparse fixed-rank (S-FR) matrix decomposition, i.e., splitting a corrupted matrix into an uncorrupted matrix of rank and a sparse ma…
Motion Estimation via Robust Decomposition with Constrained Rank
German Ros, Jose Alvarez, Julio Guerrero
In this work, we address the problem of outlier detection for robust motion estimation by using modern sparse-low-rank decompositions, i.e., Robust PCA-like methods, to impose glob…