Physics Based Computer Animation Research Papers

Review on narrow phase collision detection system in virtual environment

2025

Collision detection is one of the most important tools that has been used widely in animation and simulation especially in computer games and medical simulation purpose. One of the critical issues in collision detection system is to... more

Figure-1. Collision detection pipeline (Zachmann, 2001). object has thin surfaces or fast affecting objects that eventually missed the collision. It is important for hide- fidelity simulation even though it does not require for fast response simulation such as in computer games or animation. Thus, CCD hardly missed any potential collision between those configurations. As shown in Figure-1, the pipeline shows two general phases of collision detection pipeline which are broad phase collision detection (starting from object handler until interesting pairs block) and narrow phase collision detection (starting from grid until colliding pairs block). Generally, the main challenges of narrow phase collision detection stage when precision becomes the important element to determine the colliding pairs.

of manipulating limited type of polygon, the proposed technique should indulge a better solution for any type of object that consist of vertices, edges and surfaces. The generalized technique should be able to handle distance computation, point of contact, and penetration depth with a reasonable speed and nearly accurate computation. Discrete collision detection (DCD) works has been studied a decade ago and the pioneers of this work are (Goldsmith and Salmon, 1987; Gilbert, Johnson et al. 1988; James, 1988; Baraff, 1989; Omohundro, 1989; Gilbert and Foo, 1990; Lin and Canny, 1991) where one of the remarkable researches conducted by (Gilbert, Johnson et al. 1988). The author created an efficient technique for computing the Euclidean distance between triangles using mathematical programming method. It used the position and orientation of the object and detect any potential collision along a continuous path (refer to Figure-1). An improvement has been made later on by using a simple

Figure-3. Differences between normal oct-tree and medial axis surface introduced by Hubbard (Hubbard, 1996). Hubbard introduced a sphere tree construction by using oct-tree based type based on previous research by Quinlan (Hubbard 1996). By using space-time bound technique, Hubbard technique allowed the collision detection technique to work fast by approximation and graceful deprivation on the objects. In his dissertation, he concluded that the implementation of the technique works faster than the previous one. REFERENCES

Table-1. Techniques for distance computation. Meanwhile, point of contact technique for narrow phase collision detection was first introduced by (Gilbert, Johnson et al. 1988). The technique used simplex based implementation based on GJK but failed to satisfy the termination criteria (Gilbert and Foo 1990) that had been used to calculate the possible nearest points. Lin Canny technique was introduced then by implementing reducing resolution technique for detecting point of contact (Lin and Canny 1991). However, the technique missed certain cases where it may fall in infinite loop for complex polygons (Cameron, 1997; Chong-Jin and Gilbert, 2001; Sundaraj and Retnasamy, 2007; Shen and Li, 2010). Mirtich (1998) in (Chakraborty, Jufeng et al. 2008) proposed a V-Clip that provided solution for the Lin- Canny but not suitable when the coherence level is high. Later Ma et al (2012) proposed a distance computation technique for canal surfaces to detect point of contact but dedicated only for canal surfaces and involved complex calculations with only a few iterations and not suitable for massive models (Je, Tang et al. 2012; Yu and Yamane, 2013; Wang, Shi et al. 2014; Zhang, Kim et al., 2014). Trajectory based technique was implemented for detecting point of contact has been proposed in Schulman but according to the author itself, it involves expensive iteration and the trajectory could be missed sometimes (Schulman, Lee et al. 2013). Table-2 shows a summarized version of point of contact techniques.

Table-2. Techniques for point of contact. penetrates into other object (the object completely inside the other object) (Escande, Miossec et al. 2007). Zhang et al., (Zhang, Kim et al. 2007) provided a generalized penetration depth computation which is able to compute object penetration depth faster than other previous method mentioned in their research paper but depends heavily on pre-processing configuration with a lot of predefined (Shengzheng and Jie, 2009; Choi, Ryu et al. 2010; Zhang and Du, 2011; Zhi, Shiqiu et al. 2011; Heng, KunChao et al. 2012). Later, Hausdorff distance in (Tang, Lee et al. 2009; Barton, Hanniel et al. 2010) was used to calculate the penetration depth for polygon but depends on closed models (where there is no hole for the object) and well- defined inside and outside vertices points (Je, Tang et al. 2012; Jia, Chitta et al. 2012; Zhang, Kim et al. 2014). Table-3 summarized all our findings for penetration depth issues. Considering the last component of narrow phase collision detection, Gilbert (Gilbert, Johnson et al. 1988) proposed a fast procedure to handle penetration of narrow phase collision detection but sometimes the technique reports false positive result for potential primitives (Cameron, 1997; Ponamgi, Manocha et al. 1997). Lin- Canny technique does not offer penetrating handling technique well but with a simple technique to cover penetration depth where it involves calculating the total distance travel from one point into another point that penetrate the other object (Bergen, 2001; Kim, Otaduy ef al. 2003; Stephane and Lin, 2006). The technique itself could be a repeated cycle forever if penetration happens (Kim, Otaduy et al. 2003; Stephane and Lin, 2006; Zhang, Kim et al. 2007). Mirtich (Mirtich, 1998), developed a V- Clip complex technique that is capable to handle penetration but it is inefficient when the object completely

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Subdivision schemes for fluid flow

by Joe Warren

2024, Proceedings of the 26th annual conference on Computer graphics and interactive techniques - SIGGRAPH '99

The motion of fluids has been a topic of study for hundreds of years. In its most general setting, fluid flow is governed by a system of non-linear partial differential equations known as the Navier-Stokes equations. However, in several... more

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Evolution of Virtual Catapults

by Nicolas Chaumont

2024

We describe the re-implementation of a system to evolve the morphology and behavior of artificial creatures, originally due to Karl Sims. The new implementation is fast, flexible, and allows for the evolution of a range of novel behaviors... more

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3D deformable multiresolution interactive LV model

by maximo mero

2024, Computers in Cardiology

In this paper , we enhance existing techniques for simulating flexible volumetric objects. The idea is to use a mixed model of Finite Element and Mesh Free Methods. From this approach we will be able to build a 3D deformable model which... more

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Interactive manipulation of rigid body simulations

by Michael Erdman

2024, Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH '00

Physical simulation of dynamic objects has become commonplace in computer graphics because it produces highly realistic animations. In this paradigm the animator provides few physical parameters such as the objects' initial positions and... more

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A density-accurate tracking solution for smoke upresolution

by Pierre Poulin

2024, The Visual Computer

Controlling smoke simulations is a notoriously challenging and tedious task, usually requiring many trial-and-error iterations that prevent using expensive computations at high resolutions. Unfortunately, naïvely going from a more... more

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Efficient cloth model and collisions detection for dressing virtual people

by Bernhard Spanlang

2024

This paper describes a fast technique for dressing virtual humans with different pieces of clothing. It exploits a mass-spring cloth model but applies a new velocity modification approach to overcome its super-elasticity. The algorithm... more

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Interactive dynamic response for games

by jose medina

2024, Proceedings of the 2007 ACM SIGGRAPH symposium on Video games

Dynamic response is a technique for employing a physical reaction to an animated character. The technique utilizes a database of reactions as example motions to transition to following a dynamic simulation of an interaction. The search... more

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Tag based vocabulary as a motion language in robotics

by Manuel Rodriguez

2024

Robotics in the past was described as algorithm centric problems. The idea was that a certain computer program is able to determine the optimal action. This decision is made, according to the computational paradigm, by the CPU which is... more

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A Fast and Robust GJK Implementation for Collision Detection of Convex Objects

by Gino van den Bergen

2023, Journal Of Graphics, Gpu, And Game Tools

This paper presents an implementation of the Gilbert-Johnson-Keerthi algorithm for computing the distance between convex objects, that has improved performance, robustness, and versatility over earlier implementations. The algorithm... more

ing axis v from t = 0 is also a separating axis for tf = 1. However, v fails to be a

Figure 4: Performance under density variations

Algorithm 2 The GJK separating-axis algorithm in case of nonintersecting objects, the separating-axis algorithm performs better than the distance algorithm for another reason. Notice that the value of ||v|| is not needed in the GJK separating-axis algorithm. The computation of ||v|| involves evaluating a square root, which is an expensive operation. A single iteration of the separating-axis algorithm is therefore significantly cheaper than an iteration of the distance algorithm. needs less iterations for finding a separating axis of a pair of nonintersecting ob- jects than for computing an accurate approximation of v(A — B). For instance in Figure 1, the vector v; is a separating axis for the first ttme when k = 2. If the objects intersect, then the algorithm terminates on v; = 9, returning an inter- section. Algorithm 2 shows an algorithm for computing a separating axis, which is derived from the GJK distance algorithm. Besides requiring fewer iterations

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Sensory state machines for physically-based animation

by Augusto Vidal

2023, The Visual Computer

In this paper, we propose a flexible representation of controllers that uses high-level sensors and possesses a general and intuitive structure that offers several types of parameters, which can be modified either automatically or with... more

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Cloth Simulation Using AABB Hierarchies and GPU Parallelism

by roman salazar

2023, 2010 Brazilian Symposium on Games and Digital Entertainment

Providing realistic, high-resolution and high-fidelity representation of motions ia essential in the cloth simulation problem. In order to make high resolution simulations tractable, several algorithms have been developed that manage... more

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A robust method for real-time thread simulation

by Błażej Kubiak

2023

In this paper, we present a physically based model for real-time simulation of thread dynamics. Our model captures all the relevant aspects of the physics of the thread, including quasi-zero elasticity, bending, torsion and... more

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Detail-preserving variational surface design with multiresolution constraints

by Remi Ronfard

2023, Proceedings Shape Modeling Applications, 2004.

We present a variational framework for rapid shape prototyping. The modeled shape is represented as a Catmull-Clark multiresolution subdivision surface which is interactively deformed by direct user input. Free-form design goals are... more

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Cloth Simulation Using AABB Hierarchies and GPU Parallelism

by Roman Salazar

2023, 2010 Brazilian Symposium on Games and Digital Entertainment

Providing realistic, high-resolution and high-fidelity representation of motions ia essential in the cloth simulation problem. In order to make high resolution simulations tractable, several algorithms have been developed that manage... more

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Physical touch-up of human motions

by Hyun Joon Shin

2023, 11th Pacific Conference onComputer Graphics and Applications, 2003. Proceedings.

Many popular motion editing methods do not take physical principles into account potentially producing implausible motions. This paper introduces an efficient method for touching up edited motions to improve physical plausibility. We... more

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Fluids in deforming meshes

by James O'Brien

2023, Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation

This paper describes a simple modification to an Eulerian fluid simulation that permits the underlying mesh to deform independent of the simulated fluid's motion. The modification consists of a straightforward adaptation of the commonly... more

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Liquid simulation on lattice-based tetrahedral meshes

by James O'Brien

2023, ACM SIGGRAPH 2007 computer animation festival

We describe a method for animating incompressible liquids with detailed free surfaces. For each time step, semi-Lagrangian contouring computes a new fluid boundary (represented as a fine surface triangulation) from the previous time... more

Chentanez, Feldman, Labelle, O’Brien, and Shewchuk / Liquid Simulation on Lattice-Based Tetrahedral Meshes

Chentanez, Feldman, Labelle, O’Brien, and Shewchuk / Liquid Simulation on Lattice-Based Tetrahedral Meshes Figure 2: Each time step uses one surface triangulation and one tetrahedral mesh. A new time step’s surface S;+ is created by semi-Lagrangian advection: tracing paths back- ward in time through the velocity field on M; to locate a poini relative to S;. The isosurface stuffing algorithm generate: a tetrahedral mesh M;., (with coarser surface resolution) from S;41. Velocities are interpolated/extrapolated from M,.

Chentanez, Feldman, Labelle, O’Brien, and Shewchuk / Liquid Simulation on Lattice-Based Tetrahedral Meshes Figure 3: Top: A wave of liquid flowing over a hemispher- ical obstacle. Center: Cutaway view of the tetrahedra en- closed by the green box in the top image. Bottom: Portions of the surface triangulation and tetrahedral mesh, respec- tively, enclosed by the yellow box in the top image.

Chentanez, Feldman, Labelle, O’Brien, and Shewchuk / Liquid Simulation on Lattice-Based Tetrahedral Meshes Figure 4: A comparison of images taken from animations generated with our thickening scheme (top) and without (bottom). The example with no thickening suffers from visible holes and artifacts where thin structures vanish. Observe the difference in liquid volume between the last frames of the two sequences: without thickening, the simulation has lost appreciable volume.

Figure 5: Finding points that lie approximately on the me- dial axis. In this example, my is used but mz is rejected. Chentanez, Feldman, Labelle, O’Brien, and Shewchuk / Liquid Simulation on Lattice-Based Tetrahedral Meshes

Simulation domains with sinuous shapes make it diffi- cult to geometrically coarsen a mesh in a way suitable for multigrid—clustering unstructured tetrahedra does not cre- ate larger tetrahedra. This problem can occur even for regu- lar hexahedral grids on irregularly shaped domains. We cir- cumvent this problem with algebraic multigrid, which uses only the finite volume matrices, and not the geometry, to construct the prolongation and restriction operators. From the prolongation and restriction operators the system matri- ces can be defined by Ay_1 = Ry—1,xAgPhk-1- V-cycle multigrid begins by applying a relaxation oper- ator S(Ax,X,,b,), which returns an approximation solution (usually with poor accuracy) to the system A,x, = by. We use two Gauss-Seidel iterations. Next, it computes the resid- ual of the system, restricts it to the next coarser grid, and recursively solves the coarse problem on the restricted resid- ual. It prolongs the coarse solution and adds it, as a correc- tive term, to the fine solution. Finally, the relaxation operator is applied again. We use full-cycle multigrid, which uses V- cycle multigrid as a subroutine as follows. VCvecr eMintricein(y, h. /L)

Chentanez, Feldman, Labelle, O’Brien, and Shewchuk / Liquid Simulation on Lattice-Based Tetrahedral Meshes Figure 6: A sequence beginning with a fluid configuration in the shape of an angel. Once the simulation begins, the structure falls, creating a complex splash pattern.

figure 7: A sequence wherein a block of liquid is released at one side of a closed container. The wave of water flows over an »bstacle and crashes against the far side of the container.

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Interpolating and approximating implicit surfaces from polygon soup

by James O'Brien

2023, ACM Transactions on Graphics

This paper describes a method for building interpolating or approximating implicit surfaces from polygonal data. The user can choose to generate a surface that exactly interpolates the polygons, or a surface that approximates the input by... more

Figure 1: Interpolating and approximating surfaces (green) generated from polygonal original (brown).

Senne Se 7 ne, | EE 20 By selecting an appropriate weight function, a variety of interpolating or approximating behaviors can be achieved, even with low-order basis functions. In general, a weight function that approaches +oo at zero will cause interpola- tion. We use the weight function For the moving least-squares formulation, we allow the fit to change depending on where we evaluate the function so that c varies with x. We do so by weighting each row of Equation (1) by w(||x — p;||), where w(r) is some distance weighting function, which gives us

Figure 3: The quadrature scheme used over a triangle. Figure 2: The column on the left shows the results gener- ated using integrated polygonal constraints. The middle and right columns show the results generated with different densities of scat- tered point constraints.

The integrals will be infinite when e = O and the eval- uation point x lies precisely on a polygon. In this case, f(x) has a removable singularity at x; we can skip the least- squares step and simply set f(x) to the value ¢, dictated by the polygon. It is possible that two polygons intersect at a point where their constraints disagree, in which case f has an essential singularity at that point. Evaluating at or near such points in a numerically stable fashion is difficult. How- ever, we can sidestep the issue by setting € to an extremely small number, far below the smallest feature size relevant to a given application. p is the integration variable ranging over the polygon, and ox is the constraint value. We can choose ¢x to be constant, or we can choose ¢, to vary polynomially over each poly- gon. For later use, it is convenient to define terms with the weighting function omitted:

Unfortunately, this approach does not work as well as one might like. The additional constraints influence the func- tion’s gradient only crudely, and they can cause undesirable oscillatory behavior as the evaluation point moves away from the surface. This behavior is illustrated in the lower halt of Figure 4. It occurs because when the distance between the evaluation point and the surface point is much large than the offset distance, the inside and outside constraints effectively cancel each other out. Even if only outside (01 only inside) constraints are used, they will still effectively merge to a single average valued constraint far away. Heuris- tics, such as those described by [Ohtake et al., 2003a], can suppress some of the spurious behavior, but the value of the function far from the surface will not be useful. Further- more, these quasi-normal constraints cause severe problems when used with the approximation procedure described in the next section. Figure 4: | A one-dimensional example showing the height field generated from four position and normal constraints. The first (top) image shows the result with our method, and the arrows indicate the outward normal directions. The second shows an expanded view demonstrating far-field behavior. The third and fourth images show the results generated by pseudo-normal con- straints with linear and quadratic basis functions. The small dots indicate the placement of the inside and outside pseudo-normal constraints.

Figure 5: A two-dimensional example comparing interpolating and approximating results. The center images show input con- straints as dotted lines and the contour as a solid line. The outer images show the resulting function as a height-field.

In the case where we only use the constant basis function, so that b(a) = [1], the fit from Equation (5) simplifies to which has the very intuitive interpretation that the interpo- lating function’s value at x is simply the weighted average of the values at a predicted by each of the Sx(a).

Figure 6: The first (top) row shows the result of applying our iterative adjustment algorithm with different values of € to a polygonal scorpion model. The second row shows the original and constructed surfaces together. The third row shows the re- sult of only adjusting the surface to average values (no iterative adjustment). The fourth row shows the result generated when no correction is applied.

Figure 7: The top left image shows a polygonized version the Utah teapot which contains holes (around lid and tip of spout), and intersecting parts (handle and spout with body). The top right image is a near-interpolating surface which fills the holes and removes intersecting surfaces. The bottom row contains pho- tographs of physical models built on a fused deposition machine. The bottom right image shows a physical cutaway model.

Figure 9: The heavy-loader shown top left contains many defects that make it unsuitable for simulation as a deformable object. The approximating surface, top center, fully encloses the original model. The tetrahedral finite-element model, top right, can be used as a simulation envelope to model the effect of an impact, lower left and lower right. Figure 8: The far-left image shows a closeup view of the original polygons for the heavy-loader’s back grill. The center-left image shows the resulting interpolating surface, and the center-right a slightly approximating one. The far-right image shows a rear view of the interpolating surface for the entire loader. The dented appearance near sharp edges is a polygonization artifact.

Table 1: This table lists the computation times and € parameter for the examples used in this paper. The columns P. In and V. Out list the number (in thousands) of polygons in the input model and the number (in thousands) of vertices in the output surface. We measure ¢€ in thousandths of the diagonal length of the object’s bounding box. Computation times (minutes:seconds) measure total time on a 3GHz P4 beginning with reading the input model and ending with writing the polygonized output surface. Column groups matcl the € values used for the examples shown in the figures.

‘igure 10: A collection of polygonal models processed with our algorithm. [Continued on next page.]

‘igure 11: Additional polygonal models processed with our algorithm. [Continued from previous page.| cannot do the two-dimensional integral analytically, but the one- dimensional line integral one does have an analytic solution. TAUBIN, G. 1995. A signal processing approach to fair surface design In Proceedings of ACM SIGGRAPH 1995. 351—358.

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A method for animating viscoelastic fluids

by James O'Brien

2023, ACM Transactions on Graphics

This paper describes a technique for animating the behavior of viscoelastic fluids, such as mucus, liquid soap, pudding, toothpaste, or clay, that exhibit a combination of both fluid and solid characteristics. The technique builds upon... more

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Animating suspended particle explosions

by James O'Brien

2023, ACM Transactions on Graphics

This paper describes a method for animating suspended particle explosions. Rather than modeling the numerically troublesome, and largely invisible blast wave, the method uses a relatively stable incompressible fluid model to account for... more

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Animating gases with hybrid meshes

by James O'Brien

2023, ACM Transactions on Graphics

This paper presents a method for animating gases on unstructured tetrahedral meshes to efficiently model the interaction of fluids with irregularly shaped obstacles. Because our discretization scheme parallels that of the standard... more

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Human values and digital citizen science interactions

by Gary Hsieh

2023, International Journal of Human-Computer Studies

Sustained participation is critical to the success of digital citizen-science initiatives, yet much of the current literature focuses on mapping people's motives to engage without considering the extent to which participation is sustained... more

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Send Me a Different Message

by Gary Hsieh

2023, Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

Social systems and applications often rely on message triggers to promote, remind and even persuade people to perform certain actions. However, repeated exposure to these triggers can lead to boredom, annoyance and decreased engagement.... more

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Structural optimization based on meshless element free Galerkin and level set methods

by WAJID KHAN

2023, Computer Methods in Applied Mechanics and Engineering

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of... more

where n represents nodal points in the local support domain, B; is the strain matrix and u; and v; are the nodal displacements for j** node. In Fig. 1, a graphical representation of nodal points, virtual background cell structure, support domain or domain of influence for a computational point and 4 x 4 Gauss quadrature points are shown. Consequently, the stress vector can be

In this paper, we use the following cubic spline weight function:

Figure 3: Flow chart of proposed method. 6 Numerical experiments and discussions

Figure 4: Optimization history, for problem 1 using the EFG based optimization method (black dot: void material, red dot : solid material).

Figure 5: Optimization history, for problem 1 using the FEM based optimization method.

Figure 6: Volume and objective function convergence for test problem 1.

Figure 8: Volume and objective function convergence for test problem 2.

Figure 9: Optimization history, for problem 3 using the EFG based optimization method (black dot: void material, red dot : solid material).

Figure 10: Volume and objective function convergence for test problem 3.

Figure 11: Optimization history, for problem 4 using the EFG based optimization method (black dot: void material, red dot : solid material).

Figure 12: Volume and objective function convergence for test problem 4.

Figure 13: Optimization history, for problem 5 ing the EFG based optimization method (black dot: void material, red dot : solid material).

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To cite this version

by David Noguera

2023

HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad,... more

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Transferring Facial Expressions to Different Face Models

by Toni Susin

2023

We introduce a facial deformation system that helps the character setup process and gives artists the possibility to manipulate models as if they were using a puppet. The method uses a set of labels that define specific facial features... more

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Transferring the Rig and Animations from a Character to Different Face Models

by Toni Susin

2023, Computer Graphics Forum

We introduce a facial deformation system that allows artists to define and customize a facial rig and later apply the same rig to different face models. The method uses a set of landmarks that define specific facial features and deforms... more

Figure 1: Jn this example, we transfer the character setup from the source model (SM) to the target model (TM). The character rig includes: 19 joints and a NURBS surface behind the mouth (a) SM, (b) SM rig, (c) SM shape, (d) TM, (e) TM rig, (f) TM Shape (3D models courtesy of Dygra).

Figure 3: The generic rig: (a) 3D textured model; (b) wire- frame, 1800 points; (c) 44 landmarks; (d) weight distribu- tion; (e) 11 polygon geometric surfaces, representing the face muscles; (f) 6 bones that constitute the skeleton. Figure 2: Overview: (a) define the source and target model; (b) adapt the source model geometry to fit the target model; (c) transfer and bind the influence objects, skeleton, shapes and attributes from the source model to the target model. As a result, we obtain a model ready to animate.

Figure 4: (a) TPS warp of a generic surface based on re- duced set of sparse landmarks (S\: source surface, S>: tar- get surface, P: source landmarks, Q: target landmarks); (b) Sticking of the source surface to the target surface after ap- plying the TPS (see Section 5.3). Figure 5: Human face warping process using 10 landmarks.

Figure 7: Warping structure with (a) sparse correspon- dences (landmarks); (b) dense correspondences. Figure 6: Human face deformation with 10 landmarks, TPS front view (left); close up (middle); stick lines that represent the dense correspondence between the generic rig and the target model after applying STK (right).

Figure 8: Cartoon deformation: (a) TPS and stick lines; (b) cartoon after STK; (c) muscles transfer front view; (d) muscles transfer side view.

Figure 9: Weight distribution between generic rig and target model: (a) weight distribution for the jaw bone (red is w = 0, blue is w = 1); (b) weight region distribution for the face.

Figure 11: Method pipeline: (1) find surface correspon- dence based on the SM and TM landmarks; (2) obtain surface dense correspondence; (3) transfer attributes from the SM to the TM; (4) bind the attributes to the TM based on the SM weights.

Figure 12: Geometric surface deformation: (a) source model (SM); (b) SM TPS; (c) SM STK; (d) target model (3D models courtesy of Dygra).

Figure 13: In this example, we show 3 of the 25 shapes we transferred from the source model (SM) to the target models (TM). First row details de SM, second and third row detail the TM: (a) rest pose; (b) eye brows up; (c) left eye brow up; d) angry expression, mix of four different shapes: end of the eye brow up, middle eye brow down, inside eye brow down and upper chick up (3D models courtesy of Dygra).

Figure 14: Jn this example, the source model and the target models were hand sculpted by an artist from Dygra Films. The ri, from the source model was also created by an artist from Dygra films. The rig includes 12 bones distributed along the head seven bones around the mouth and one influence object. The influence object is a NURBS surface located behind the mouth wer the seven mouth bones are attached. The source model rig was automatically transferred and bound to the target models usin; the method described in Section 5. The source model (a) has 1455 points, target model (e) has 1610 points and target model (i has 1291 points (3D models courtesy of Dygra).

Figure 15: Distance between muscle and skin surfaces on the generic rig and on the model: (a) scanned human; (b) cartoon.

Figure 16: This example shows keyframes from a facial animation that reproduces a speech. To create these animations, an artist created an animation script in the source model (first and forth column), based on mixing different blendshapes and manipulating the joints of the mouth and NURBS surface. Then the animation script was applied to both target models (second, third, ffith and sixth column) (3D models and animations courtesy of Dygra).

Figure 17: This example shows keyframes from a facial animation that tests extreme expressions. To create these animations an artist created an animation script in the source model (first and forth column), based on mixing different blendshapes and manipulating the joints of the mouth and NURBS surface. Then the animation script was applied to both target models (second, third, fifth and sixth column) (3D models and animations courtesy of Dygra).

Figure 18: Generic rig: (a) textured; (b) wireframe; (c) 44 landmarks; (d) muscles.

Figure 19: Human (a) textured; (b) wireframe; (c) 10 landmarks; (d) muscles.

Figure 20: Cartoon (a) textured; (b) wireframe; (c) 44 landmarks; (d) muscles.

Figure 21: Animal (a) textured; (b) wireframe; (c) 44 landmarks; (d) muscles.

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Interactive dynamic response for games

by jose medina

2023, Proceedings of the 2007 ACM SIGGRAPH symposium on Video games

Dynamic response is a technique for employing a physical reaction to an animated character. The technique utilizes a database of reactions as example motions to transition to following a dynamic simulation of an interaction. The search... more

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Parametric Skeletons with Reduced Soft‐Tissue Deformations

by cristian romero

2023, Computer Graphics Forum

Figure 1: We augment parametric skeletal models with reduced soft-tissue deformations. We formulate dynamics in an unposed reference space (left) and design a sparse biharmonic subspace to represent soft-tissue deformation. The local... more

Figure 2: This figure highlights the deformations produced by the data-driven skeletal model in our formulation (left: without pose- based blend shapes; right: with pose-based blend-shapes). Our de- formation model matches exactly the data-driven result on static, contact-free poses. J. Tapia, C. Romero, J. Pérez, M. Otaduy / Parametric Skeletons with Reduced Soft-Tissue Deformations

Figure 3: On a skeletal model such as the one described in Sec- tion 3.1, contact forces are fully absorbed by the skeleton (left). With our augmented soft model described in Section 3.3, contact induces soft-tissue deformations (right).

Figure 4: Volumetric discretization of the soft-tissue layer for both the SMPL (left) and MANO (right) models. Meshes have been gen- erated using a Delaunay tetrahedralization conformal to boundary surfaces [Sil5]. In the former case, we construct the inner sur- face displacing the outer surface with smoothly varying thickness throughout the body. In the latter case, we use the mesh of a real hand skeleton as the inner surface. Our method only needs these meshes during preprocessing, for the computation of the subspace basis and the cubature weights.

Figure 5: This figure shows the distribution of subspace handle points defined for the body (right) and the hand (left) respectively.

Figure 6: Our soft skeleton body model matches static data-driven deformations accurately for a given pose (left). When in contact with an external object it produces a dynamic response with two- way coupling (right). Soft-tissue deformation is clearly noticeable in the unposed reference shape (inset). Local static deformations due to the pose blend shape (e.g., arm bulging) apply independently of external interactions.

Figure 7: Our handling of inertial forces correctly captures soft- tissue deformations induced by the skeletal motion. The picture shows two frames of an animation with a character subject to ro- tational acceleration (left) and deceleration (right) in the vertical axis, and the resulting colormap of soft-tissue deformations.

J. Tapia, C. Romero, J. Pérez, M. Otaduy / Parametric Skeletons with Reduced Soft-Tissue Deformations Figure 8: A parametric approximation of the hand is realistically deformed due to skeletal poses and contact with objects. The pic- ture shows four frames of an animation sequence and a colormap with the corresponding vertex displacements in unposed space (in- sets). Sliding over a structure of fine cylinders produces ripples on the palm (see supplementary video).

Figure 9: Our model incorporates skeletal tracking of target input animations through control forces. Note that even in the absence of external interactions, dynamic deformations appear as an effect of inertial components. The figure shows a sequence of three animation frames and a colormap with the corresponding vertex displacements in unposed space (insets).

Figure 10: Fingers sliding sideways along a cylindrical shape. The resulting deformation of the fingertips is shown in unposed refer- ence shape. The colormap indicates the first right Cauchy-Green tensor invariant; note the difference between stretch (red) and com- pression (blue) at the sides of the fingertips depending on the effect of sliding direction.

Figure 11: Interactive hand tracking demo using the Leap Motion. The user is capable of touching and grasping environment objects, producing significant soft-tissue deformations as an effect of fric- tional contact.

Figure 12: Each row presents a sequence of pictures showing two rag-doll characters with different shape parameters undergo the same simulation scenario. The bodies are slowly pushed by a cylinder while hanging from the wrists, resulting in different deformation behaviors.

Figure 13: We compare the behavior of our method (top) against the ground-truth full-space problem (bottom). While the full-space solution is slightly smoother, our approximation produces a plausi- ble behavior at a computational cost orders of magnitude lower. whose deformation is controlled by s = 10 shape parameters and p = 144 pose parameters. To define the soft-tissue layer, we follow the approach described in [ROCP20]; here, the inner surface is defined by displacing the vertices of the template mesh a distance

Figure 14: We compare our handle basis (center) and a modal basis (right) on the augmented MANO model. As reference, we also show the same scene without soft-tissue deformation (left). The modal basis has difficulty producing the correct bulging on the side of the hand, and i. suffers artifacts on the finger tips, far from the contact area, due to the global support of the basis.

Figure 16: The density of handles determines the ability to resolve contacts with high spatial frequency. In the images, we press a fin- ger pad of the augmented MANO model with a pin, and we show the handles in black. The contact is resolved more accurately as the pin approaches a handle (left), and smoothly degrades as the pin moves away from the handles (right).

Table 1: Performance comparison of a full-space simulation vs. our method, with different numbers of cubature points (denotes as “subspace X”). The table shows average timings per time step, as well as broken down into the evaluation of forces, the evaluation of the energy Hessian, and the linear system solve (all in ms). As shown in Figure 6, the resulting skeletal model accurately matches data-driven static deformations while at the same time re- alistically responding to external interactions. The deformation of the skeleton can be driven by an input animation through tracking forces (Figure 9), and the soft-tissue dynamically deforms accord- ingly due to the effect of inertial forces (Figure 7). Interestingly, as shown in Figure 12, the parametric model allows us to reproduce

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