Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

To facilitate the analysis of human actions, interactions and emotions, we compute a 3D model of human body pose, hand pose, and facial expression from a single monocular image. To achieve this, we use thousands of 3D scans to train a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with fully articulated hands and an expressive face. Learning to regress the parameters of SMPL-X directly from images is challenging without paired images and 3D ground truth. Consequently, we follow the approach of SMPLify, which estimates 2D features and then optimizes model parameters to fit the features. We improve on SMPLify in several significant ways: (1) we detect 2D features corresponding to the face, hands, and feet and fit the full SMPL-X model to these; (2) we train a new neural network pose prior using a large MoCap dataset; (3) we define a new interpenetration penalty that is both fast and accurate; (4) we automatically detect gender and the appropriate body models (male, female, or neutral); (5) our PyTorch implementation achieves a speedup of more than 8x over Chumpy. We use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild. We evaluate 3D accuracy on a new curated dataset comprising 100 images with pseudo ground-truth. This is a step towards automatic expressive human capture from monocular RGB data. The models, code, and data are available for research purposes at https://smpl-x.is.tue.mpg.de.Comment: To appear in CVPR 201

Optimization of Tower Crane Location and Material Quantity Between Supply and Demand Points: A Comparative Study

Location optimization of tower crane as an expensive equipment in the construction projects has an important effect on material transportation costs. Due to the construction site conditions, there are several tower crane location optimization models. Appropriate location of tower cranes for material supply and engineering demands is a combinatorial optimization problem within the tower crane layout problem that is difficult to resolve.  Meta-heuristics are popular and useful techniques to resolve complex optimization problems. In this paper, the performance of the Particle Swarm Optimization (PSO) and four newly developed meta-heuristic algorithms Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO), Vibrating Particles System (VPS), and Enhanced Vibrating Particles System (EVPS) are compared in terms of their effectiveness in resolving a practical Tower Crane Layout (TCL) problem. Results show that ECBO performs better than other three methods in both cases

Optimal Design of Steel-Concrete Composite I-girder Bridges Using Three Meta-Heuristic Algorithms

Bridges are among very important structures in engineering, due to their rather high cost, and this is why optimization of these structures is a challenging problem. In this paper, optimal design of steel-concrete composite I-girder bridges is performed. Three recently developed meta-heuristic algorithms consisting of Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO) and Vibration Particle System (VPS) are utilized for the first time in the optimal design of steel-concrete I-girder bridges. Both continuous and discrete variables are utilized in the process of optimization. Performance and the convergence histories of these algorithms are compared. In order to have a suitable comparison between these algorithms with previous algorithms, PSO is used and results are displayed. This paper focuses on cost optimization the bridges. Furthermore constraints include all of requirements of the code of practice for design. The comparative study has shown that VPS algorithm has better performance than CBO and ECBO. However, all three algorithms act in a way that the final optimized design does not need the addition of the longitudinal stiffener

Fuzzy-multi-mode Resource-constrained Discrete Time-cost-resource Optimization in Project Scheduling Using ENSCBO

Construction companies are required to employ effective methods of project planning and scheduling in today's competitive environment. Time and cost are critical factors in project success, and they can vary based on the type and amount of resources used for activities, such as labor, tools, and materials. In addition, resource leveling strategies that are used to limit fluctuations in a project's resource consumption also affect project time and cost. The multi-mode resource-constrained discrete-time–cost-resource optimization (MRC-DTCRO) is an optimization tool that is developed for scheduling of a set of activities involving multiple execution modes with the aim of minimizing time, cost, and resource moment. Moreover, uncertainty in cost should be accounted for in project planning because activities are exposed to risks that can cause delays and budget overruns. This paper presents a fuzzy-multi-mode resource-constrained discrete-time–cost-resource optimization (F-MRC-DTCRO) model for the time-cost-resource moment tradeoff in a fuzzy environment while satisfying all the project constraints. In the proposed model, fuzzy numbers are used to characterize the uncertainty of direct cost of activities. Using this model, different risk acceptance levels of the decision maker can be addressed in the optimization process. A newly developed multi-objective optimization algorithm called ENSCBO is used to search non-dominated solutions to the fuzzy multi-objective model. Finally, the developed model is applied to solve a benchmark test problem. The results indicate that incorporating the fuzzy structure of uncertainty in costs to previously developed MRC-DTCRO models facilitates the decision-making process and provides more realistic solutions

CBO and CSS Algorithms for Resource Allocation and Time-Cost Trade-Off

Resource allocation project scheduling problem (RCPSP) has been one of the challenging subjects among researchers in the last decades. Though several methods have been adopted to solve this problem, however, new metahuristics are available to solve this problem for finding better solution with less computational time. In this paper two new metahuristic algorithms are applied for solving this problem known as charged system search (CSS) and colliding body optimization (CBO). The results show that both of these algorithms find reasonable solutions, however CBO could find the result in a less computational time having a better quality. Two case studies are conducted to evaluate the performance and applicability of the proposed algorithms

A Comparative Study of the Optimum Tuned Mass Damper for High-rise Structures Considering Soil-structure Interaction

The present paper focuses on the optimum design of tuned mass damper (TMD) as a device for control of the structures. The optimum free vibration parameters such as period and damping ratio depend on the soil condition. For this reason, the seven meta-heuristic algorithms namely colliding bodies optimization (CBO), enhanced colliding bodies optimization (ECBO), water strider algorithm (WSA), dynamic water strider algorithm (DWSA), ray optimization (RO) algorithm, teaching-learning-based optimization (TLBO) algorithm and plasma generation optimization (PGO) are used to find the TMD parameters considering soil-structure interaction (SSI) effects. These optimization methods are applied to a benchmark 40-story structure. For comparison, the obtained results of these algorithms are compared. The capability and robustness of the algorithms are investigated through the benchmark problem. The results are shown that the soil type affects the optimum values of the TMD parameters, especially for the soft soil. To evaluate the performance of the obtained parameters in both the frequency and time domains, time history displacement and acceleration transfer function of the top story of the structure are calculated for the model with and without considering the SSI effects

Optimal Design of the Monopole Structures Using the CBO and ECBO Algorithms

Tubular steel monopole structure is widely used for supporting antennas in telecommunication industries. This research presents two recently developed meta-heuristic algorithms, which are called Colliding Bodies Optimization (CBO) and Enhanced Colliding Bodies Optimization (ECBO), for size optimization of monopole steel structures. The design procedure aims to obtain minimum weight of monopole structures subjected to the TIA-EIA222F specification. Two monopole structure examples are examined to verify the suitability of the design procedure and to demonstrate the effectiveness and robustness of the CBO and ECBO in creating optimal design for this problem. The outcomes of the enhanced colliding bodies optimization (ECBO) are also compared to those of the standard colliding bodies optimization (CBO) to illustrate the importance of the enhancement of the CBO algorithm