Publications de Pascal Fua | EPFL Graph Search

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In this paper, we propose a face reconstruction technique that produces models that not only look good when texture mapped, but are also metrically accurate. Our method is designed to work with short uncalibrated video or movie sequences, even when the lighting is poor resulting in specularities and shadows that complicate the algorithm's task. Our approach relies on optimizing the shape parameters of a sophisticated PCA based model given pairwise image correspondences as input. All that is required is enou

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections ...

We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a si ...

We propose a new approach to semi-automated delineation of curvilinear structures in a wide range of imaging modalities. Earlier approaches lack robustness to imaging noise, do not provide radius estimates for the structures and operate only on single channel images. In contrast, ours makes use of the color information, when available, and generates accurate centreline location and radius estimates with minimal supervision. We demonstrate this on a wide range of datasets ranging from a 2D dataset of aerial

Representing motions as linear sums of principal components has become a widely accepted animation technique. While powerful, the simplest version of this approach is not particularly well suited to modeling the specific style of an individual whose motion had not yet been recorded when building the database: It would take an expert to adjust the PCA weights to obtain a motion style that is indistinguishable from his. Consequently, when realism is required, current practice is to perform a full motion captu

We present a novel approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, our method builds a set of candidate trees over many different subsets of point ...

Machine-learning based classification techniques have been shown to be effective at detecting objects in complex scenes. However, the final results are often obtained from the alarms produced by the classifiers through a post-processing which typically rel ...

Rho guanosine triphosphatases (GTPases) control the cytoskeletal dynamics that power neurite outgrowth. This process consists of dynamic neurite initiation, elongation, retraction, and branching cycles that are likely to be regulated by specific spatiotemporal signaling networks, which cannot be resolved with static, steady-state assays. We present Neu - riteTracker, a computer-vision approach to automatically segment and track neuronal morphodynamics in time-lapse datasets. Feature extraction then quantifi

Visual object counting is a fundamental computer vision task underpinning numerous real-world applications, from cell counting in biomedicine to traffic and wildlife monitoring. However, existing methods struggle to handle the challenge of stacked 3D objects in which most objects are hidden by those above them. To address this important yet underexplored problem, we propose a novel 3D counting approach that decomposes the task into two complementary subproblems - estimating the 3D geometry of the object sta

Limited-angle electron tomography (ET) aims to reconstruct 3D shapes from 2D projections of transmission electron microscopy (TEM) within a restricted range and number of tilting angles, but it suffers from the missing-edge problem that causes severe reconstruction artefacts. Deep learning approaches have shown promising results in alleviating these artefacts, yet they typically require large high-quality training datasets with known 3D ground truth, which are difficult to obtain in electron microscopy. To

The difficulty of obtaining annotations to build training databases still slows down the adoption of recent deep learning approaches for biomedical image analysis. In this paper, we show that we can train a Deep Net to perform 3D volumetric delineation giv ...

We introduce a novel local image descriptor designed for dense wide-baseline matching purposes. We feed our descriptors to a graph-cuts based dense depth map estimation algorithm and this yields better wide-baseline performance than the commonly used corre ...

We present a non-rigid registration technique that achieves spatial, photometric, and visibility accuracy. It lets us photo-realistically augment 3D deformable surfaces under complex illumination conditions and in spite of severe occlusions. There are many ...

We formulate a model for multi-class object detection in a multi-camera environment. From our knowledge, this is the first time that this problem is addressed taken into account different object classes simultaneously. Given several images of the scene taken from different angles, our system estimates the ground plane location of the objects from the output of several object detectors applied at each viewpoint. We cast the problem as an energy minimization modeled with a Conditional Random Field (CRF). Inst

Image matching is a fundamental problem in Computer Vision. In the context of feature-based matching, SIFT and its variants have long excelled in a wide array of applications. However, for ultra-wide baselines, as in the case of aerial images captured unde ...

Cascade-style approaches to implementing ensemble classifiers can deliver significant speed-ups at test time. While highly effective, they remain challenging to tune and their overall performance depends on the availability of large validation sets to esti ...

Binary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptors. They have been shown with promising results on some real time application ...

Current virtual reality technologies provide many ways to interact with virtual humans. Most of those techniques, however, are limited to synthetic elements and require cumbersome sensors. We have combined a real-time simulation and rendering platform with a real-time, non-invasive vision-based recognition system to investigate interactions in a mixed environment with real and synthetic elements. In this paper, we present the resulting system, the example of a checkers game between a real person and an auto

In this paper, we improve upon earlier approaches to segmenting mitochondria in Electron Microscopy images by explicitly modeling the double membrane that encloses mitochondria, as well as using features that capture context over an extended neighborhood. We demonstrate that this results in both improved classification accuracy and reduced computational requirements for training.

Each smile is unique: one person surely smiles in different ways (e.g. closing/opening the eyes or mouth). Given one input image of a neutral face, can we generate multiple smile videos with distinctive characteristics? To tackle this one-to-many video gen ...

Pascal Fua

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Accurate Face Models from Uncalibrated and Ill-Lit Video Sequences

Multi-view Tracking UsingWeakly Supervised Human Motion Prediction

Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras

Semi-Automated Reconstruction of Curvilinear Structures in Noisy 2D images and 3D image stacks

Mahalanobis Motion Generation

Delineating Trees in Noisy 2D Images and 3D Image Stacks

Principled Detection-by-Classification from Multiple Views

Computer vision profiling of neurite outgrowth dynamics reveals spatio-temporal modularity of Rho GTPase signaling

Counting Stacked Objects

Limited-angle tomography reconstruction via projector guided 3D diffusion

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

A fast local descriptor for dense matching

Retexturing in the Presence of Complex Illumination and Occlusions

Conditional Random Fields for Multi-Camera Object Detection

Learning to Match Aerial Images with Deep Attentive Architectures

Fast Object Detection with Entropy-Driven Evaluation

Do We Need Binary Features for 3D Reconstruction?

Augmented reality for real and virtual humans

Exploiting Enclosing Membranes and Contextual Cues for Mitochondria Segmentation

Every Smile is Unique: Landmark-Guided Diverse Smile Generation