11,616 research outputs found

    Automatic facial expression tracking for 4D range scans

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    This paper presents a fully automatic approach of spatio-temporal facial expression tracking for 4D range scans without any manual interventions (such as specifying landmarks). The approach consists of three steps: rigid registration, facial model reconstruction, and facial expression tracking. A Scaling Iterative Closest Points (SICP) algorithm is introduced to compute the optimal rigid registration between a template facial model and a range scan with consideration of the scale problem. A deformable model, physically based on thin shells, is proposed to faithfully reconstruct the facial surface and texture from that range data. And then the reconstructed facial model is used to track facial expressions presented in a sequence of range scans by the deformable model

    Automatic 3D facial model and texture reconstruction from range scans

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    This paper presents a fully automatic approach to fitting a generic facial model to detailed range scans of human faces to reconstruct 3D facial models and textures with no manual intervention (such as specifying landmarks). A Scaling Iterative Closest Points (SICP) algorithm is introduced to compute the optimal rigid registrations between the generic model and the range scans with different sizes. And then a new template-fitting method, formulated in an optmization framework of minimizing the physically based elastic energy derived from thin shells, faithfully reconstructs the surfaces and the textures from the range scans and yields dense point correspondences across the reconstructed facial models. Finally, we demonstrate a facial expression transfer method to clone facial expressions from the generic model onto the reconstructed facial models by using the deformation transfer technique

    Shape basis interpretation for monocular deformable 3D reconstruction

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    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a novel interpretable shape model to encode object non-rigidity. We first use the initial frames of a monocular video to recover a rest shape, used later to compute a dissimilarity measure based on a distance matrix measurement. Spectral analysis is then applied to this matrix to obtain a reduced shape basis, that in contrast to existing approaches, can be physically interpreted. In turn, these pre-computed shape bases are used to linearly span the deformation of a wide variety of objects. We introduce the low-rank basis into a sequential approach to recover both camera motion and non-rigid shape from the monocular video, by simply optimizing the weights of the linear combination using bundle adjustment. Since the number of parameters to optimize per frame is relatively small, specially when physical priors are considered, our approach is fast and can potentially run in real time. Validation is done in a wide variety of real-world objects, undergoing both inextensible and extensible deformations. Our approach achieves remarkable robustness to artifacts such as noisy and missing measurements and shows an improved performance to competing methods.Peer ReviewedPostprint (author's final draft

    Inconsistency in 9 mm bullets : correlation of jacket thickness to post-impact geometry measured with non-destructive X-ray computed tomography

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    Fundamental to any ballistic armour standard is the reference projectile to be defeated. Typically, for certification purposes, a consistent and symmetrical bullet geometry is assumed, however variations in bullet jacket dimensions can have far reaching consequences. Traditionally, characteristics and internal dimensions have been analysed by physically sectioning bullets – an approach which is of restricted scope and which precludes subsequent ballistic assessment. The use of a non-destructive X-ray computed tomography (CT) method has been demonstrated and validated Kumar et al., 2011); the authors now apply this technique to correlate bullet impact response with jacket thickness variations. A set of 20 bullets (9 mm DM11) were selected for comparison and an image-based analysis method was employed to map jacket thickness and determine the centre of gravity of each specimen. Both intra- and inter-bullet variations were investigated, with thickness variations of the order of 200 um commonly found along the length of all bullets and angular variations of up to 50 um in some. The bullets were subsequently impacted against a rigid flat plate under controlled conditions (observed on a high-speed video camera) and the resulting deformed projectiles were re-analysed. The results of the experiments demonstrate a marked difference in ballistic performance between bullets from different manufacturers and an asymmetric thinning of the jacket is observed in regions of pre-impact weakness. The conclusions are relevant for future soft armour standards and provide important quantitative data for numerical model correlation and development. The implications of the findings of the work on the reliability and repeatability of the industry standard V50 ballistic test are also discussed

    A bayesian approach to simultaneously recover camera pose and non-rigid shape from monocular images

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    © . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper we bring the tools of the Simultaneous Localization and Map Building (SLAM) problem from a rigid to a deformable domain and use them to simultaneously recover the 3D shape of non-rigid surfaces and the sequence of poses of a moving camera. Under the assumption that the surface shape may be represented as a weighted sum of deformation modes, we show that the problem of estimating the modal weights along with the camera poses, can be probabilistically formulated as a maximum a posteriori estimate and solved using an iterative least squares optimization. In addition, the probabilistic formulation we propose is very general and allows introducing different constraints without requiring any extra complexity. As a proof of concept, we show that local inextensibility constraints that prevent the surface from stretching can be easily integrated. An extensive evaluation on synthetic and real data, demonstrates that our method has several advantages over current non-rigid shape from motion approaches. In particular, we show that our solution is robust to large amounts of noise and outliers and that it does not need to track points over the whole sequence nor to use an initialization close from the ground truth.Peer ReviewedPostprint (author's final draft
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