Towards Automatic Human Body Model Fitting to a 3D Scan

in D'APUZZO, Nicola (Ed.) Proceedings of 3DBODY.TECH 2017 - 8th International Conference and Exhibition on 3D Body Scanning and Processing Technologies, Montreal QC, Canada, 11-12 Oct. 2017 (2017, October)

This paper presents a method to automatically recover a realistic and accurate body shape of a person wearing clothing from a 3D scan. Indeed, in many practical situations, people are scanned wearing ... [more ▼]

This paper presents a method to automatically recover a realistic and accurate body shape of a person wearing clothing from a 3D scan. Indeed, in many practical situations, people are scanned wearing clothing. The underlying body shape is thus partially or completely occluded. Yet, it is very desirable to recover the shape of a covered body as it provides non-invasive means of measuring and analysing it. This is particularly convenient for patients in medical applications, customers in a retail shop, as well as in security applications where suspicious objects under clothing are to be detected. To recover the body shape from the 3D scan of a person in any pose, a human body model is usually fitted to the scan. Current methods rely on the manual placement of markers on the body to identify anatomical locations and guide the pose fitting. The markers are either physically placed on the body before scanning or placed in software as a postprocessing step. Some other methods detect key points on the scan using 3D feature descriptors to automate the placement of markers. They usually require a large database of 3D scans. We propose to automatically estimate the body pose of a person from a 3D mesh acquired by standard 3D body scanners, with or without texture. To fit a human model to the scan, we use joint locations as anchors. These are detected from multiple 2D views using a conventional body joint detector working on images. In contrast to existing approaches, the proposed method is fully automatic, and takes advantage of the robustness of state-of-art 2D joint detectors. The proposed approach is validated on scans of people in different poses wearing garments of various thicknesses and on scans of one person in multiple poses with known ground truth wearing close-fitting clothing. [less ▲]

Training Very Deep Networks via Residual Learning with Stochastic Input Shortcut Connections

in 24th International Conference on Neural Information Processing, Guangzhou, China, November 14–18, 2017 (2017, July 31)

Many works have posited the benefit of depth in deep networks. However, one of the problems encountered in the training of very deep networks is feature reuse; that is, features are ’diluted’ as they are ... [more ▼]

Many works have posited the benefit of depth in deep networks. However, one of the problems encountered in the training of very deep networks is feature reuse; that is, features are ’diluted’ as they are forward propagated through the model. Hence, later network layers receive less informative signals about the input data, consequently making training less effective. In this work, we address the problem of feature reuse by taking inspiration from an earlier work which employed residual learning for alleviating the problem of feature reuse. We propose a modification of residual learning for training very deep networks to realize improved generalization performance; for this, we allow stochastic shortcut connections of identity mappings from the input to hidden layers.We perform extensive experiments using the USPS and MNIST datasets. On the USPS dataset, we achieve an error rate of 2.69% without employing any form of data augmentation (or manipulation). On the MNIST dataset, we reach a comparable state-of-the-art error rate of 0.52%. Particularly, these results are achieved without employing any explicit regularization technique. [less ▲]

Deformation Based Curved Shape Representation

in IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)

In this paper, we introduce a deformation based representation space for curved shapes in Rn. Given an ordered set of points sampled from a curved shape, the proposed method represents the set as an ... [more ▼]

In this paper, we introduce a deformation based representation space for curved shapes in Rn. Given an ordered set of points sampled from a curved shape, the proposed method represents the set as an element of a finite dimensional matrix Lie group. Variation due to scale and location are filtered in a preprocessing stage, while shapes that vary only in rotation are identified by an equivalence relationship. The use of a finite dimensional matrix Lie group leads to a similarity metric with an explicit geodesic solution. Subsequently, we discuss some of the properties of the metric and its relationship with a deformation by least action. Furthermore, invariance to reparametrization or estimation of point correspondence between shapes is formulated as an estimation of sampling function. Thereafter, two possible approaches are presented to solve the point correspondence estimation problem. Finally, we propose an adaptation of k-means clustering for shape analysis in the proposed representation space. Experimental results show that the proposed representation is robust to uninformative cues, e.g. local shape perturbation and displacement. In comparison to state of the art methods, it achieves a high precision on the Swedish and the Flavia leaf datasets and a comparable result on MPEG-7, Kimia99 and Kimia216 datasets. [less ▲]

STARR - Decision SupporT and self-mAnagement system for stRoke survivoRs Vision based Rehabilitation System

in European Project Space on Networks, Systems and Technologies (2017)

This chapter explains a vision based platform developed within a European project on decision support and self-management for stroke survivors. The objective is to provide a low cost home rehabilitation ... [more ▼]

This chapter explains a vision based platform developed within a European project on decision support and self-management for stroke survivors. The objective is to provide a low cost home rehabilitation system. Our main concern is to maintain the patients' physical activity while carrying a continuous monitoring of his physical and emotional state. This is essential for recovering some autonomy in daily life activities and preventing a second damaging stroke. Post-stroke patients are initially subject to physical therapy under the supervision of a health professional to follow up on their daily physical activity and monitor their emotional state. However, due to social and economical constraints, home based rehabilitation is eventually suggested. Our vision platform paves the way towards having low cost home rehabilitation. [less ▲]

Video-Based Feedback for Assisting Physical Activity

in 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) (2017)

In this paper, we explore the concept of providing feedback to a user moving in front of a depth camera so that he is able to replicate a specific template action. This can be used as a home based ... [more ▼]

In this paper, we explore the concept of providing feedback to a user moving in front of a depth camera so that he is able to replicate a specific template action. This can be used as a home based rehabilitation system for stroke survivors, where the objective is for patients to practice and improve their daily life activities. Patients are guided in how to correctly perform an action by following feedback proposals. These proposals are presented in a human interpretable way. In order to align an action that was performed with the template action, we explore two different approaches, namely, Subsequence Dynamic Time Warping and Temporal Commonality Discovery. The first method aims to find the temporal alignment and the second one discovers the interval of the subsequence that shares similar content, after which standard Dynamic Time Warping can be used for the temporal alignment. Then, feedback proposals can be provided in order to correct the user with respect to the template action. Experimental results show that both methods have similar accuracy rate and the computational time is a decisive factor, where Subsequence Dynamic Time Warping achieves faster results. [less ▲]

DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS

in IEEE International Conference on Image Processing, Beijing 17-20 Spetember 2017 (2017)

Similarity Metric For Curved Shapes In Euclidean Space

in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (2016, June 26)

In this paper, we introduce a similarity metric for curved shapes that can be described, distinctively, by ordered points. The proposed method represents a given curve as a point in the deformation space ... [more ▼]

In this paper, we introduce a similarity metric for curved shapes that can be described, distinctively, by ordered points. The proposed method represents a given curve as a point in the deformation space, the direct product of rigid transformation matrices, such that the successive action of the matrices on a fixed starting point reconstructs the full curve. In general, both open and closed curves are represented in the deformation space modulo shape orientation and orientation preserving diffeomorphisms. The use of direct product Lie groups to represent curved shapes led to an explicit formula for geodesic curves and the formulation of a similarity metric between shapes by the $L^{2}$-norm on the Lie algebra. Additionally, invariance to reparametrization or estimation of point correspondence between shapes is performed as an intermediate step for computing geodesics. Furthermore, since there is no computation of differential quantities on the curves, our representation is more robust to local perturbations and needs no pre-smoothing. We compare our method with the elastic shape metric defined through the square root velocity (SRV) mapping, and other shape matching approaches [less ▲]

Visual and human-interpretable feedback for assisting physical activity

in European Conference on Computer Vision (ECCV) Workshop on Assistive Computer Vision and Robotics Amsterdam, (2016)

Physical activity is essential for stroke survivors for recovering some autonomy in daily life activities. Post-stroke patients are initially subject to physical therapy under the supervision of a health ... [more ▼]

Physical activity is essential for stroke survivors for recovering some autonomy in daily life activities. Post-stroke patients are initially subject to physical therapy under the supervision of a health professional, but due to economical aspects, home based rehabilitation is eventually suggested. In order to support the physical activity of stroke patients at home, this paper presents a system for guiding the user in how to properly perform certain actions and movements. This is achieved by presenting feedback in form of visual information and human-interpretable messages. The core of the proposed approach is the analysis of the motion required for aligning body-parts with respect to a template skeleton pose, and how this information can be presented to the user in form of simple recommendations. Experimental results in three datasets show the potential of the proposed framework. [less ▲]

A Revisit to Human Action Recognition from Depth Sequences: Guided SVM-Sampling for Joint Selection

in IEEE Winter Conference on Applications of Computer Vision (WACV), 2016 (2016)

Detecting Credit Card Fraud using Periodic Features

in IEEE International Conference on Machine Learning and Applications (2015, December)