Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks

3D Morphable Model (3DMM) based methods have achieved great success in recovering 3D face shapes from single-view images. However, the facial textures recovered by such methods lack the fidelity as exhibited in the input images. Recent work demonstrates high-quality facial texture recovering with generative networks trained from a large-scale database of high-resolution UV maps of face textures, which is hard to prepare and not publicly available. In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild, without the need to capture a large-scale face texture database. The main idea is to refine the initial texture generated by a 3DMM based method with facial details from the input image. To this end, we propose to use graph convolutional networks to reconstruct the detailed colors for the mesh vertices instead of reconstructing the UV map. Experiments show that our method can generate high-quality results and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.Comment: Accepted to CVPR 2020. The source code is available at https://github.com/FuxiCV/3D-Face-GCN

Stochastic Systems with Cumulative Prospect Theory

Stochastic control problems arise in many fields. Traditionally, the most widely used class of performance criteria in stochastic control problems is risk-neutral. More recent attempts at introducing risk-sensitivity into stochastic control problems include the application of utility functions. The decision theory community has long debated the merits of using expected utility for modeling human behaviors, as exemplified by the Allais paradox. Substantiated by strong experimental evidence, Cumulative Prospect Theory (CPT) based performance measures have been proposed as alternatives to expected utility based performance measures for evaluating human-centric systems. Our goal is to study stochastic control problems using performance measures derived from the cumulative prospect theory. The first part of this thesis solves the problem of evaluating Markov decision processes (MDPs) using CPT-based performance measures. A well-known method of solving MDPs is dynamic programming, which has traditionally been applied with an expected utility criterion. When the performance measure is CPT-inspired, several complications arise. Firstly, when solving a problem via dynamic programming, it is important that the performance criterion has a recursive structure, which is not true for all CPT-based criteria. Secondly, we need to prove the traditional optimality criteria for the updated problems (i.e., MDPs with CPT-based performance criteria). The theorems stated in this part of the thesis answer the question: what are the conditions required on a CPT-inspired criterion such that the corresponding MDP is solvable via dynamic programming? The second part of this thesis deals with stochastic global optimization problems. Using ideas from the cumulative prospect theory, we are able to introduce a novel model-based randomized optimization algorithm: Cumulative Weighting Optimization (CWO). The key contributions of our research are: 1) proving the convergence of the algorithm to an optimal solution given a mild assumption on the initial condition; 2) showing that the well-known cross-entropy optimization algorithm is a special case of CWO-based algorithms. To the best knowledge of the author, there is no previous convergence proof for the cross-entropy method. In practice, numerical experiments have demonstrated that a CWO-based algorithm can find a better solution than the cross-entropy method. Finally, in the future, we would like to apply some of the ideas from cumulative prospect theory to games. In this thesis, we present a numerical example where cumulative prospect theory has an unexpected effect on the equilibrium points of the classic prisoner's dilemma game

Strategy Selection for Product Service Systems Using Case-based Reasoning

A product service system integrates products and services in order to lower environmental impact. It can achieve good eco-efficiency and has received increase in the last decade. This study focuses on strategy selection for product service system design. Case-based reasoning is utilized to provide suggestions for finding an appropriate strategy. To build a case database, successful PSS cases from the literature and websites were collected and formulated. Twelve indices under three categories were analyzed and selected to describe cases. A lot of successful PSS cases and their information were collected. Forty seven cases were used in this study because of the completeness of information. The analytic hierarchic process is used to find the relative weights of the factors that relate to the selection of customers. These weights are used in calculating the similarity in the case-based reasoning process. The successful strategy of the most similar case is extracted and recommended for PSS strategy determination. More than 90% of tested cases obtained an appropriate strategy from the most similar case. Finally, two new products are introduced to find the best strategy for product service system design and development using the proposed case-based reasoning system

One-loop renormalization of the chiral Lagrangian for spinless matter fields in the SU(N) fundamental representation

We perform the leading one-loop renormalization of the chiral Lagrangian for spinless matter fields living in the fundamental representation of SU(N). The Lagrangian can also be applied to any theory with a spontaneous symmetry breaking of $SU(N)_L\times SU(N)_R$ to $SU(N)_V$ and spinless matter fields in the fundamental representation. For QCD, the matter fields can be kaons or pseudoscalar heavy mesons. Using the background field method and heat kernel expansion techniques, the divergences of the one-loop effective generating functional for correlation functions of single matter fields are calculated up to $\mathcal{O}(p^3)$. They are absorbed by counterterms not only from the third order but also from the second order chiral Lagrangian.Comment: 13 page

Implications of chiral symmetry on SS-wave pionic resonances and the scalar charmed mesons

The chiral symmetry of QCD requires energy-dependent pionic strong interactions at low energies. This constraint, however, is not fulfilled by the usual Breit--Wigner parameterization of pionic resonances, leading to masses larger than the real ones. We derive relations between nonleptonic three-body decays of the $B$-meson into a $D$-meson and a pair of light pseudoscalar mesons based on SU(3) chiral symmetry. Employing effective field theory methods, we demonstrate that taking into account the final-state interactions, the experimental data of the decays $B^-\to D^+\pi^-\pi^-$, $B_s^0\to \bar{D}^0K^-\pi^+$, $B^0\to\bar{D}^0\pi^-\pi^+$, $B^-\to D^+\pi^-K^-$ and $B^0\to\bar{D}^0\pi^-K^+$ can all be described by the nonperturbative $\pi/\eta/K$-$D/D_s$ scattering amplitudes previously obtained from a combination of chiral effective field theory and lattice QCD calculations. The results provide a strong support of the scenario that the broad scalar charmed meson $D^\ast_0(2400)$ should be replaced by two states, the lower one of which has a mass of around 2.1 GeV, much smaller than that extracted from experimental data using a Breit--Wigner parameterization.Comment: 26 pages, 9 figuere

Basic Mathematics for Biochemists. by Athel Cornish-Bowden

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