Fit and sizing of clothing are fundamental problems in the field of garment design, manufacture, and retail. Here we propose new computational methods for adjusting the fit of clothing on realistic models of the human body by interactively modifying desired fit attributes. Clothing fit represents the relationship between the body and the garment, and can be quantified using physical fit attributes such as ease and pressure on the body. However, the relationship between pattern geometry and such fit attributes is notoriously complex and nonlinear, requiring deep pattern making expertise to adjust patterns to achieve fit goals. Such attributes can be computed by physically based simulations, using soft avatars. Here we propose a method to learn the relationship between the fit attributes and the space of 2D pattern edits. We demonstrate our method via interactive tools that directly edit fit attributes in 3D and instantaneously predict the corresponding pattern adjustments. The approach has been tested with a range of garment types, and validated by comparing with physical prototypes. Our method introduces an alternative way to directly express fit adjustment goals, making pattern adjustment more broadly accessible. As an additional benefit, the proposed approach allows pattern adjustments to be systematized, enabling better communication and audit of decisions.

Deep generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models have demonstrated their efficacy in generating 2D images and 3D meshes. However, interpreting and controlling the learned latent space is very difficult, severely limiting the utility of these methods. Worse, it has been shown that fully disentangling the latent space using only unsupervised methods is theoretically infeasible. In this work, we introduce a novel method for latent space disentanglement on 3D meshes that achieves interpretability, control, and strong disentanglement. Our method comprises two components: a learned feature function for predicting 3D mesh features, and a generative model that predicts not only the desired meshes but also their features and feature gradients. We employ feature gradients as part of the loss function to promote disentanglement. Experimental results demonstrate that our disentanglement method is highly effective and achieves strong disentanglement without compromising the accuracy of the reconstruction.

Dynamics simulation with frictional contacts is important for a wide range of applications, from cloth simulation to object manipulation. Recent methods using smoothed lagged friction forces have enabled robust and differentiable simulation of elastodynamics with friction. However, the resulting frictional behavior can be inaccurate and may not converge to analytic solutions. Here we evaluate the accuracy of lagged friction models in comparison with implicit frictional contact systems. We show that major inaccuracies near the stick-slip threshold in such systems are caused by lagging of friction forces rather than by smoothing the Coulomb friction curve. Furthermore, we demonstrate how systems involving implicit or lagged friction can be correctly used with higher-order time integration and highlight limitations in earlier attempts. We demonstrate how to exploit forward-mode automatic differentiation to simplify and, in some cases, improve the performance of the inexact Newton method. Finally, we show that other complex phenomena can also be simulated effectively while maintaining smoothness of the entire system. We extend our method to exhibit stick-slip frictional behavior and preserve volume on compressible and nearly-incompressible media using soft constraints.

Measuring friction on the human body is challenging on areas where the surface of the skin is curved. Handheld devices for measuring friction may make it easier to rapidly measure from hard-to-reach areas, however, the orientation of the probe relative to the surface is often sub-optimal to reliably measure the normal force. Here, the friction on the surfaces of a mannequin and a human body are measured and corrected using a curvature correction technique. This is accomplished by accounting for the shape of the measurement surface and the orientation of the measurement device relative to that surface. Two strategies for constructing the surface shape of the measurement surface are compared.

In situ measurement of frictional properties, particularly of human skin, is challenging. A major challenge is that the measured surface may be curved and oriented arbitrarily relative to the measurement device, making it difficult to obtain accurate and reliable friction estimates. Here we propose a method to simultaneously estimate the orientation of the surface during friction measurements, using a custom-made hand-held tribometer. The method accounts for the position and orientation of the tribometer relative to the shape of the measured surface. Our simple calibration method allows lightweight tribometers to be built with cheaper materials like plastic, without sacrificing accuracy.

We propose a method to improve the robustness of state-of-art learning-based methods for finding point-to-point correspondences of 3D human models with anthropometric landmarks.Specifically, current deep learning-based methods generally focus on intrinsic, local, properties of body shapes, which lack extrinsic global information.Thus, these methods are challenged by matching ambiguities, for instance, due to the bilateral symmetry of human body shapes.We demonstrate our method with an unsupervised learning-based method, DeepShells [5].Our work introduces a landmark supervision method based on the Shells by adding linear soft constraints to minimize this problem that we term the "intrinsic feature ambiguity problem."To that end, we derive a simple but efficient pipeline that better distinguishes self-similarities yet has similar overall matching quality.

We examine a variety of numerical methods that arise when considering dynamical systems in the context of physics-based simulations of deformable objects. Such problems arise in various applications, including animation, robotics, control and fabrication. The goals and merits of suitable numerical algorithms for these applications are different from those of typical numerical analysis research in dynamical systems. Here the mathematical model is not fixed a priori but must be adjusted as necessary to capture the desired behaviour, with an emphasis on effectively producing lively animations of objects with complex geometries. Results are often judged by how realistic they appear to observers (by the "eye-norm") as well as by the efficacy of the numerical procedures employed. And yet, we show that with an adjusted view numerical analysis and applied mathematics can contribute significantly to the development of appropriate methods and their analysis in a variety of areas including finite element methods, stiff and highly oscillatory ODEs, model reduction, and constrained optimization.

Physics-based simulation methods for deformable objects suffer limitations due to the conflicting requirements that are placed on them. The work horse semi-implicit (SI) backward Euler method is very stable and inexpensive, but it is also a blunt instrument. It applies heavy damping, which depends on the timestep, to all solution modes and not just to high-frequency ones. As such, it makes simulations less lively, potentially missing important animation details. At the other end of the scale, exponential methods (exponential Rosenbrock Euler (ERE)) are known to deliver good approximations to all modes, but they get prohibitively expensive and less stable for very stiff material. In this article, we devise a hybrid, semi-implicit method called SIERE that allows the previous methods SI and ERE to each perform what they are good at. To do this, we employ at each timestep a partial spectral decomposition, which picks the lower, leading modes, applying ERE in the corresponding subspace. The rest is handled (i.e., effectively damped out) by SI. No solution of nonlinear algebraic equations is required throughout the algorithm. We show that the resulting method produces simulations that are visually as good as those of the exponential method at a computational price that does not increase with stiffness, while displaying stability and damping with respect to the high-frequency modes. Furthermore, the phenomenon of occasional divergence of SI is avoided.

Simulation of human soft tissues in contact with their environment is essential in many fields, including visual effects and apparel design. Biological tissues are nearly incompressible. However, standard methods employ compressible elasticity models and achieve incompressibility indirectly by setting Poisson's ratio to be close to 0.5. This approach can produce results that are plausible qualitatively but inaccurate quantatively. This approach also causes numerical instabilities and locking in coarse discretizations or otherwise poses a prohibitive restriction on the size of the time step. We propose a novel approach to alleviate these issues by replacing indirect volume preservation using Poisson's ratios with direct enforcement of zonal volume constraints, while controlling fine-scale volumetric deformation through a cell-wise compression penalty. To increase realism, we propose an epidermis model to mimic the dramatically higher surface stiffness on real skinned bodies. We demonstrate that our method produces stable realistic deformations with precise volume preservation but without locking artifacts. Due to the volume preservation not being tied to mesh discretization, our method also allows a resolution consistent simulation of incompressible materials. Our method improves the stability of the standard neo-Hookean model and the general compression recovery in the Stable neo-Hookean model.

Frictional contact between deformable elastic objects remains a difficult simulation problem in computer graphics. Traditionally, contact has been resolved using sophisticated collision detection schemes and methods that build on the assumption that contact happens between polygons. While polygonal surfaces are an efficient representation for solids, they lack some intrinsic properties that are important for contact resolution. Generally, polygonal surfaces are not equipped with an intrinsic inside and outside partitioning or a smooth distance field close to the surface. Here we propose a new method for resolving frictional contacts against deforming implicit surface representations that addresses these problems. We augment a moving least squares (MLS) implicit surface formulation with a local kernel for resolving contacts, and develop a simple parallel transport approximation to enable transfer of frictional impulses. Our variational formulation of dynamics and elasticity enables us to naturally include contact constraints, which are resolved as one Newton-Raphson solve with linear inequality constraints. We extend this formulation by forwarding friction impulses from one time step to the next, used as external forces in the elasticity solve. This maintains the decoupling of friction from elasticity thus allowing for different solvers to be used in each step. In addition, we develop a variation of staggered projections, that relies solely on a non-linear optimization without constraints and does not require a discretization of the friction cone. Our results compare favorably to a popular industrial elasticity solver (used for visual effects), as well as recent academic work in frictional contact, both of which rely on polygons for contact resolution. We present examples of coupling between rigid bodies, cloth and elastic solids.

We present two practical methods for measurement of spectral skin reflectance suited for live subjects, and drive a spectral BSSRDF model with appropriate complexity to match skin appearance in photographs, including human faces. Our primary measurement method employs illuminating a subject with two complementary uniform spectral illumination conditions using a multispectral LED sphere to estimate spatially varying parameters of chromophore concentrations including melanin and hemoglobin concentration, melanin blend-type fraction, and epidermal hemoglobin fraction. We demonstrate that our proposed complementary measurements enable higher-quality estimate of chromophores than those obtained using standard broadband illumination, while being suitable for integration with multiview facial capture using regular color cameras. Besides novel optimal measurements under controlled illumination, we also demonstrate how to adapt practical skin patch measurements using a hand-held dermatological skin measurement device, a Miravex Antera 3D camera, for skin appearance reconstruction and rendering. Furthermore, we introduce a novel approach for parameter estimation given the measurements using neural networks which is significantly faster than a lookup table search and avoids parameter quantization. We demonstrate high quality matches of skin appearance with photographs for a variety of skin types with our proposed practical measurement procedures, including photorealistic spectral reproduction and renderings of facial appearance.

The 2020 CHCCS Achievement Award from the Canadian Human-Computer Communications Society is presented to Prof. Dinesh Pai (UBC) for his numerous high-impact contributions to the field of computer graphics research. His diverse research addresses physics-based animation, multi-sensory displays including haptics and sound, and realistic digital human models. CHCCS invites a publication by the award winner to be included in the proceedings, and this year we continue the tradition of an interview format rather than a formal paper. This permits a casual discussion of the research areas, insights, and contributions of the award winner. What follows is an edited transcript of a conversation between Dinesh Pai and Doug James (Stanford CS Prof and former PhD student) that took place on 14 March, 2020, via Zoom. The Interview Doug: Hello Dinesh. Congratulations on being recognized as the 2020 CHCCS Achievement Award winner.

Human motion capture using video-based or sensor-based methods gives animators the capability to directly translate complex human motions to create lifelike character animations. Advances in motion capture algorithms have improved their accuracy for estimating human generalized motion coordinates (joint angles and body positions). However, the traditional motion capture pipeline is not well suited to measure short duration, high acceleration impacts, such as running and jumping footstrikes. While high acceleration impacts have minimal influence on generalized coordinates, they play a big role in exciting soft tissue dynamics. Here we present a method for correcting motion capture trajectories using a sparse set of inertial measurement units (IMUs) collecting at high sampling rates to produce more accurate impact accelerations without sacrificing accuracy of the generalized coordinates representing gross motions. We demonstrate the efficacy of our method by correcting human motion captured experimentally using commercial motion capture systems with high rate IMUs sampling at 400Hz during basketball jump shots and running. With our method, we automatically corrected 185 jumping impacts and 1266 running impacts from 5 subjects. Post correction, we found an average increase of 84.6% and 91.1% in pelvis vertical acceleration and ankle dorsiflexion velocity respectively for basketball jump shots, and an average increase of 110% and 237% in pelvis vertical acceleration and ankle plantarflexion velocity respectively for running. In both activities, pelvis vertical position and ankle angle had small corrections on average below 2.0cm and 0.20rad respectively. Finally, when driving a human rig with soft tissue dynamics using corrected motions, we found a 143.4% and 11.2% increase in soft tissue oscillation amplitudes in basketball jump shots and running respectively. Our methodology can be generalized to correct impact accelerations for other body segments, and provide new tools to create realistic soft tissue animations during dynamic activities for more lifelike characters and better motion reconstruction for biomechanical analyses.

Elastodynamic system simulation is a key procedure in computer graphics and robotics applications. To enable these simulations, the governing differential system is discretized in space (employing FEM) and then in time. For many simulation-based applications keeping the spatial resolution of the computational mesh effectively coarse is crucial for securing acceptable computational efficiency. However, this can introduce numerical stiffening effects that impede visual accuracy. We propose and demonstrate, for both linear and nonlinear force models, a new method called EigenFit that improves the consistency and accuracy of the lower energy, primary deformation modes, as the spatial mesh resolution is coarsened. EigenFit applies a partial spectral decomposition, solving a generalized eigenvalue problem in the leading mode subspace and then replacing the first several eigenvalues of the coarse mesh by those of the fine one at rest. EigenFit's performance relies on a novel subspace model reduction technique which restricts the spectral decomposition to finding just a few of the leading eigenmodes. We demonstrate its efficacy on a number of objects with both homogenous and heterogenous material distributions.

Realistic appearance modeling of human skin is an important research topic with a variety of application in computer graphics. Various diffusion based BSSRDF models [Jensen et al. 2001, Donner and Jensen 2005, Donner and Jensen 2006] have been introduced in graphics to efficiently simulate subsurface scattering in skin including modeling its layered structure. These models however assume homogeneous subsurface scattering parameters and produce spatial color variation using an albedo map. In this work, we build upon the spectral scattering model of [Donner and Jensen 2006] and target a practical measurement-based rendering approach for such a spectral BSSRDF. The model assumes scattering in the two primary layers of skin (epidermis and dermis respectively) can be modeled with relative melanin and hemoglobin chromophore concentrations respectively. To drive this model for realistic rendering, we employ measurements of skin patches using an off-the-shelf Miravex Antera 3D camera which provides spatially varying maps of these chromophore concentrations as well as corresponding 3D surface geometry (see Figure 1) using a custom imaging setup.

Motion in depth is commonly misperceived in Virtual Reality (VR), making it difficult to intercept moving objects, for example, in games. We investigate whether motion cues could be modified to improve these interactions in VR. We developed a time-to-contact estimation task, in which observers ($n=18$n=18) had to indicate by button press when a looming virtual object would collide with their head. We show that users consistently underestimate speed. We construct a user-specific model of motion-in-depth perception, and use this model to propose a novel method to modify monocular depth cues tailored to the specific user, correcting individual response errors in speed estimation. A user study was conducted in a simulated baseball environment and observers were asked to hit a looming baseball back in the direction of the pitcher. The study was conducted with and without intervention and demonstrates the effectiveness of the method in reducing interception errors following cue modifications. The intervention was particularly effective at fast ball speeds where performance is most limited by the user's sensorimotor constraints. The proposed approach is easy to implement and could improve the user experience of interacting with dynamic virtual environments.

Simulating how the human body deforms in contact with external objects, tight clothing, or other humans is of central importance to many fields. Despite great advances in numerical methods, the material properties required to accurately simulate the body of a real human have been sorely lacking. Here we show that mechanical properties of the human body can be directly measured using a novel hand-held device. We describe a complete pipeline for measurement, modeling, parameter estimation, and simulation using the finite element method. We introduce a phenomenological model (the sliding thick skin model) that is effective for both simulation and parameter estimation. Our data also provide new insights into how the human body actually behaves. The methods described here can be used to create personalized models of an individual human or of a population. Consequently, our methods have many potential applications in computer animation, product design, e-commerce, and medicine.

Improper activation of the quadriceps muscles vastus medialis (VM) and vastus lateralis (VL) has been implicated in the development of patellofemoral pain (PFP). This explanation of PFP assumes that VM and VL produce opposing mediolateral forces on the patella. Although studies have provided evidence for opposing actions of VM and VL on the patella, other studies have suggested that their actions might be similar. In this study, we took advantage of the experimental accessibility of the rat to directly measure the forces on the patella produced by VM and VL. We found that VM and VL produce opposing mediolateral forces on the patella when the patella was lifted away from the femur. These distinct mediolateral forces were not transmitted to the tibia, however: forces measured at the distal tibia were very similar for VM and VL. Further, when the patella was placed within the trochlear groove, the forces on the patella produced by VM and VL were very similar to one another. These results suggest that mediolateral forces produced by VM and VL are balanced by reaction forces from the trochlear groove and so are not transmitted to the tibia. These results provide a rich characterization of the mechanical actions of VM and VL and have implications about the potential role of these muscles in PFP and their neural control during behavior.

Subject-specific musculoskeletal models are increasingly used in biomedical applications to predict endpoint forces due to muscle activation, matching predicted forces to experimentally observed forces at a specific limb configuration. However, it is difficult to precisely measure the limb configuration at which these forces are observed. The consequent uncertainty in limb configuration might contribute to errors in model predictions. We therefore evaluated how uncertainties in limb configuration measurement contributed to errors in force prediction, using data from in vivo measurements in the rat hindlimb. We used a data-driven approach to estimate the uncertainty in estimated limb configuration and then used this configuration uncertainty to evaluate the consequent uncertainty in force predictions, using Monte Carlo simulations. We used subject-specific models of joint structures (i.e., centers and axes of rotation) in order to estimate limb configurations for each animal. The standard deviation of the distribution of predicted force directions resulting from configuration uncertainty was small, ranging between 0.27° and 3.05° across muscles. For most muscles, this standard deviation was considerably smaller than the error between observed and predicted forces (between 0.57° and 70.96°), suggesting that uncertainty in limb configuration could not explain inaccuracies in model predictions. Instead, our results suggest that inaccuracies in muscle model parameters, most likely in parameters specifying muscle moment arms, are the main source of prediction errors by musculoskeletal models in the rat hindlimb.