Progressive Shape Models

International audienceIn this paper we address the problem of recovering both the topology and the geometry of a deformable shape using temporal mesh sequences. The interest arises in multi-camera applications when unknown natural dynamic scenes are captured. While several approaches allow recovery of shape models from static scenes, few consider dynamic scenes with evolving topology and without prior knowledge. In this nonetheless generic situation, a single time observation is not necessarily enough to infer the correct topology of the observed shape and evidences must be accumulated over time in order to learn this topology and to enable temporally consistent modelling. This appears to be a new problem for which no formal solution exists. We propose a principled approach based on the assumption that the observed objects have a fixed topology. Under this assumption, we can progressively learn the topology meanwhile capturing the deformation of the dynamic scene. The approach has been successfully experimented on several standard 4D datasets and we believe that it paves the way to more general multi-view scene capture and analysis.Dans cet article nous nous concentrons sur un problème récurrent des systèmes d'acquisition 4D : l'apprentissage de la géométrie et de la topologie d'une scène déformable à partir d'une séquence temporelle de maillages. Il s'agit d'une étape fondamentale dans le traitement de scènes naturelles et dynamiques. Tandis que de nombreux travaux ont été menés pour la reconstruction de scènes statiques, assez peu considèrent le cas de scènes dynamiques dont la topologie évolue et sans connaissances \apriori. Dans cette situation, une simple observation à un unique instant de temps n'est souvent pas suffisante pour retrouver entièrement l'information de topologie propre à la scène observée. Il semble ainsi évident que les indices sur la forme doivent être accumulés intelligemment sur une séquence complète afin d'acquerir une information aussi complète que possible sur la topologie de la scène et permettre l'apprentissage d'un modèle cohérent à la fois spatialement et temporellement. A notre connaissance cela semble un problème nouveau pour lequel aucune solution formelle n'a été proposée. Nous formulons dans cette thèse un principe de solution basé sur l'hypothèse que les objets composant la scène observée possèdent une topologie fixe. A partir de cette hypothèse de base nous pouvons progressivement apprendre la topologie et en parallèle capturer les déformations d'une scène dynamique. Les travaux présentés dans cette partie visent à retrouver une information de basse fréquence sur la géométrie de la scène. En l'état actuel, la méthode que nous proposons ne peut pas être directement utilisée pour accumuler les informations de bas niveau (détails de la surface) sur une séquence de maillages

BLiSS: Bootstrapped Linear Shape Space

Morphable models are fundamental to numerous human-centered processes as they offer a simple yet expressive shape space. Creating such morphable models, however, is both tedious and expensive. The main challenge is establishing dense correspondences across raw scans that capture sufficient shape variation. This is often addressed using a mix of significant manual intervention and non-rigid registration. We observe that creating a shape space and solving for dense correspondence are tightly coupled -- while dense correspondence is needed to build shape spaces, an expressive shape space provides a reduced dimensional space to regularize the search. We introduce BLiSS, a method to solve both progressively. Starting from a small set of manually registered scans to bootstrap the process, we enrich the shape space and then use that to get new unregistered scans into correspondence automatically. The critical component of BLiSS is a non-linear deformation model that captures details missed by the low-dimensional shape space, thus allowing progressive enrichment of the space.Comment: 12 pages, 10 figure

Generative models of the human connectome

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.Comment: 38 pages, 5 figures + 19 supplemental figures, 1 tabl

SVA: Shape variation analyzer

Temporo-mandibular osteo arthritis (TMJ OA) is characterized by progressive cartilage degradation and subchondral bone remodeling. The causes of this pathology remain unclear. Current research efforts are concentrated in finding new biomarkers that will help us understand disease progression and ultimately improve the treatment of the disease. In this work, we present Shape Variation Analyzer (SVA), the goal is to develop a noninvasive technique to provide information about shape changes in TMJ OA. SVA uses neural networks to classify morphological variations of 3D models of the mandibular condyle. The shape features used for training include normal vectors, curvature and distances to average models of the condyles. The selected features are purely geometric and are shown to favor the classification task into 6 groups generated by consensus between two clinician experts. With this new approach, we were able to accurately classify 3D models of condyles. In this paper, we present the methods used and the results obtained with this new tool

3D Shape Interpolation

Tato práce se zabývá tvorbou nástroje pro postupnou změnu tvaru trojrozměrného polygonálního modelu. Použité metody se inspirují zejména technikou Manual Landmarks a~využívají parametrizace objektu na kouli. Výsledkem práce je zásuvný modul do volně šiřitelného modelovacího programu Blender, který automaticky vytvoří animaci změny tvaru ze dvou vstupních modelů.This thesis deals with creation of a~tool for progressive interpolation of shape of a~3D polygonal mesh. Used methods take inspiration mainly in technique Manual Landmarks and use parametrization of an object to a~sphere. The result of this thesis is an addon for an open-source modelling program Blender, which automatically creates an animation of shape deformation based on two input models.

Metric Features of a Dipolar Model

The lattice spin model, with nearest neighbor ferromagnetic exchange and long range dipolar interaction, is studied by the method of time series for observables based on cluster configurations and associated partitions, such as Shannon entropy, Hamming and Rohlin distances. Previous results based on the two peaks shape of the specific heat, suggested the existence of two possible transitions. By the analysis of the Shannon entropy we are able to prove that the first one is a true phase transition corresponding to a particular melting process of oriented domains, where colored noise is present almost independently of true fractality. The second one is not a real transition and it may be ascribed to a smooth balancing between two geometrical effects: a progressive fragmentation of the big clusters (possibly creating fractals), and the slow onset of a small clusters chaotic phase. Comparison with the nearest neighbor Ising ferromagnetic system points out a substantial difference in the cluster geometrical properties of the two models and in their critical behavior.Comment: 20 pages, 15 figures, submitted to JPhys

How Do Rights Revolutions Occur? Free Speech and the First Amendment

Does law shape values? We test a model of law and norms using an area of law where economic incentives are arguably not the prime drivers of social change. From 1958–2008, Democratic judges were more likely than Republicans to favor progressive free speech standards. Using the random assignment of U.S. federal court judges setting geographically-local precedent, we estimate that progressive free speech standards liberalized sexual attitudes and behaviors and increased both crime rates and the spread of sexually transmitted diseases. We then randomly allocated data entry workers to enter newsarticles of court decisions. Progressive decisions liberalized sexual attitudes and shifted norm perceptions for data entry subjects, but not self-reported behavior. These results present evidence of law’s expressive power – with fundamental implications for decision making in social and political settings and for the empirical predictions of theoretical models in these domains

How Do Rights Revolutions Occur? Free Speech and the First Amendment

Does law shape values? We test a model of law and norms using an area of law where economic incentives are arguably not the prime drivers of social change. From 1958–2008, Democratic judges were more likely than Republicans to favor progressive free speech standards. Using the random assignment of U.S. federal court judges setting geographically-local precedent, we estimate that progressive free speech standards liberalized sexual attitudes and behaviors and increased both crime rates and the spread of sexually transmitted diseases. We then randomly allocated data entry workers to enter newsarticles of court decisions. Progressive decisions liberalized sexual attitudes and shifted norm perceptions for data entry subjects, but not self-reported behavior. These results present evidence of law’s expressive power – with fundamental implications for decision making in social and political settings and for the empirical predictions of theoretical models in these domains

Progressive Text-to-3D Generation for Automatic 3D Prototyping

Text-to-3D generation is to craft a 3D object according to a natural language description. This can significantly reduce the workload for manually designing 3D models and provide a more natural way of interaction for users. However, this problem remains challenging in recovering the fine-grained details effectively and optimizing a large-size 3D output efficiently. Inspired by the success of progressive learning, we propose a Multi-Scale Triplane Network (MTN) and a new progressive learning strategy. As the name implies, the Multi-Scale Triplane Network consists of four triplanes transitioning from low to high resolution. The low-resolution triplane could serve as an initial shape for the high-resolution ones, easing the optimization difficulty. To further enable the fine-grained details, we also introduce the progressive learning strategy, which explicitly demands the network to shift its focus of attention from simple coarse-grained patterns to difficult fine-grained patterns. Our experiment verifies that the proposed method performs favorably against existing methods. For even the most challenging descriptions, where most existing methods struggle to produce a viable shape, our proposed method consistently delivers. We aspire for our work to pave the way for automatic 3D prototyping via natural language descriptions