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Position-based dynamics has emerged as an exceedingly popular approach for animating soft body dynamics. Unfortunately, the basic approach suffers from artificial loss of angular momentum. We propose a simple approach to preserve global linear and angular momenta of bodies by directly tracking these quantities and adjusting velocities to ensure they are preserved. This approach entails negligible computational cost, requires less than 25 lines of code, and exactly preserves global linear and angular momenta.

We describe the gamification of a soft physics simulator. We developed a game, called Jelly Dude, that allows the player to change and modify the game engine by tinkering with various physics parameters, creating custom game levels and installing scripts. The game engine is capable of simulating soft-body physics and can display the simulation results visually in real-time. In order to ensure high quality graphics in real time, we have implemented intelligent computational offloading to the cloud using Jordan Neural Network (JNN) with a fuzzy logic scheme for short time prediction of network traffic between a client and a cloud server. The experimental results show that computation offloading allowed us to increase the speed of graphics rendering in terms of frames per second, and to improve the precision of soft body modeling in terms of the number of particles used to represent a soft body.

The solution of large sparse systems of linear constraints is at the base of most interactive solvers for physically-based animation of soft body dynamics. We focus on applications with hard and tight per-frame resource budgets, such as video games, where the solution of soft body dynamics needs to be computed in a few milliseconds. Linear iterative methods are preferred in these cases since they provide approximate solutions within a given error tolerance and in a short amount of time. We present a parallel randomized Gauss-Seidel method which can be effectively employed to enable the animation of 3D soft objects discretized as large and irregular triangular or tetrahedral meshes. At the beginning of each frame, we partition the set of equations governing the system using a randomized graph coloring algorithm. The unknowns in the equations belonging to the same partition are independent of each other. Then, all the equations belonging to the same partition are solved at the same time in parallel. Our algorithm runs completely on the GPU and can support changes in the constraints topology. We tested our method as a solver for soft body dynamics within the Projective Dynamics and Position Based Dynamics frameworks. We show how the algorithmic simplicity of this iterative strategy enables great numerical stability and fast convergence speed, which are essential features for physically based animations with fixed and small hard time budgets. Compared to the state of the art, we found our method to be faster and scale better while providing stabler solutions for very small time budgets.

Soft materials are not only highly deformable but they also possess rich and diverse body dynamics. Soft body dynamics exhibit a variety of properties, including nonlinearity, elasticity, and potentially infinitely many degrees of freedom. Here we demonstrate that such soft body dynamics can be employed to conduct certain types of computation. Using body dynamics generated from a soft silicone arm, we show that they can be exploited to emulate functions that require memory and to embed robust closed-loop control into the arm. Our results suggest that soft body dynamics have a short-term memory and can serve as a computational resource. This finding paves the way toward exploiting passive body dynamics for control of a large class of underactuated systems., 22 pages, 11 figures; email address corrected

Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which an abnormal formation of the rib cage gives the chest a caved-in or sunken appearance. Today, the surgical correction of this deformity is carried out in children and adults through Nuss technic, which consists in the placement of a prosthetic bar under the sternum and over the ribs. Although this technique has been shown to be safe and reliable, not all patients have achieved adequate cosmetic outcome. This often leads to psychological problems and social stress, before and after the surgical correction. This paper targets this particular problem by presenting a method to predict the patient surgical outcome based on pre-surgical imagiologic information and chest skin dynamic modulation. The proposed approach uses the patient pre-surgical thoracic CT scan and anatomical-surgical references to perform a 3D segmentation of the left ribs, right ribs, sternum and skin. The technique encompasses three steps: a) approximation of the cartilages, between the ribs and the sternum, trough b-spline interpolation; b) a volumetric mass spring model that connects two layers - inner skin layer based on the outer pleura contour and the outer surface skin; and c) displacement of the sternum according to the prosthetic bar position. A dynamic model of the skin around the chest wall region was generated, capable of simulating the effect of the movement of the prosthetic bar along the sternum. The results were compared and validated with patient postsurgical skin surface acquired with Polhemus FastSCAN system.

Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which an abnormal formation of the rib cage gives the chest a caved-in or sunken appearance. Today, the surgical correction of this deformity is carried out in children and adults through Nuss technic, which consists in the placement of a prosthetic bar under the sternum and over the ribs. Alth ough this technique has been shown to be safe and reliable, not all patients have achieved adequate cosmetic outcome. This often leads to psyc hological problems and social stress, before and after the surgical correction. This paper targets this particular problem by presenting a method to predict the patient surgical outcome based on pre-surgical imagiologic information and chest skin dynamic modulation. The proposed approach uses the patient pre-surgical thoracic CT scan and anatomical-surg ical references to perform a 3D segmentation of the left ribs, right ribs, sternum and skin. The technique encompasses three steps: a) approximation of the cartilages, between the ribs and the sternum, trough b-spline interpolation; b) a volu metric mass spring model that connects two layers - inner skin layer based on the outer pleura contour and the outer su rface skin; and c) displacement of the sternum according to the prosthetic bar position. A dynamic model of the skin around the chest wall region was generated, capable of simulating the effect of the movement of the prosthetic bar along the sternum. The results were compared and validated with patient postsurgical skin surface acquired with Polhemus FastSCAN system., Fundação para a Ciência e Tecnologia

The implicit approach can be used to efficiently model realistically deformable objects for large constraints such as stiffness or time. In soft bodies with pressure forces, models are composed of mesh points connected by springs and applied with pressure forces. System parameters such as spring constant, damping constant, etc. are defined to describe the behaviors of the deformable objects. Since the simulation of soft bodies with pressure force fails when the constraints are large, this paper proposes the method to solve this problem by using the implicit integration method for soft bodies with particular force. The results show that our method can realistically simulate the soft bodies when large constraints are applied