Physically-based Constraints Research Articles

Automatic skinning and weight retargeting of articulated characters using extended position-based dynamics

Animating an articulated character requires the explicit specification of interior skeleton structure and its attachment to skin surface. This task of “rigging” typically involves the manual weight painting and deformation fine-tuning with popular conventional animation methods. Weight painting is unavoidably a time-consuming and laborious process that would need sophisticated skills from animators during animation production. In this paper, using the extended position-based dynamics (PBD), we have articulated a strategy to generate the realistic skin deformation and reuse the painted weights on a new character. For each frame, the skin is deformed by the linear blend skinning method (LBS) at first. To solve the problem of candy-wrapper effect and surface overlapping in LBS, we improve the traditional PBD method by adding energy constraints and employ several geometrically and physically-based constraints to refine the deformed skin automatically. To further reduce the animator’s workload from tedious rigging process, we propose the weight retargeting approach using surface matching and interpolation based on the powerful bi-harmonic distance. It could transfer the weights of an existing model to a new character with similar topology. Through numerous experiments, we could demonstrate the visual performance of our new techniques on a variety of articulated characters.

Constraining snowmelt in a temperature-index model using simulated snow densities

Current snowmelt parameterisation schemes are largely untested in warmer maritime snowfields, where physical snow properties can differ substantially from the more common colder snow environments. Physical properties such as snow density influence the thermal properties of snow layers and are likely to be important for snowmelt rates. Existing methods for incorporating physical snow properties into temperature-index models (TIMs) require frequent snow density observations. These observations are often unavailable in less monitored snow environments. In this study, previous techniques for end-of-season snow density estimation (Bormann et al., 2013) were enhanced and used as a basis for generating daily snow density data from climate inputs. When evaluated against 2970 observations, the snow density model outperforms a regionalised density-time curve reducing biases from −0.027 g cm−3 to −0.004 g cm−3 (7%). The simulated daily densities were used at 13 sites in the warmer maritime snowfields of Australia to parameterise snowmelt estimation. With absolute snow water equivalent (SWE) errors between 100 and 136 mm, the snow model performance was generally lower in the study region than that reported for colder snow environments, which may be attributed to high annual variability. Model performance was strongly dependent on both calibration and the adjustment for precipitation undercatch errors, which influenced model calibration parameters by 150–200%. Comparison of the density-based snowmelt algorithm against a typical temperature-index model revealed only minor differences between the two snowmelt schemes for estimation of SWE. However, when the model was evaluated against snow depths, the new scheme reduced errors by up to 50%, largely due to improved SWE to depth conversions. While this study demonstrates the use of simulated snow density in snowmelt parameterisation, the snow density model may also be of broad interest for snow depth to SWE conversion. Overall, the study responds to recent calls for broader testing of TIMs across different snow environments, improves existing snow modelling in Australia and proposes a new method for introducing physically-based constraints on snowmelt rates in data-poor regions.

A method based on local approximate solutions (LAS) for inverting transient flow in heterogeneous aquifers

Summary An inverse method based on local approximate solutions (LAS inverse method) is proposed to invert transient flows in heterogeneous aquifers. Unlike the objective-function-based inversion techniques, the method does not require forward simulations to assess measurement-to-model misfits; thus the knowledge of aquifer initial conditions (IC) and boundary conditions (BC) is not required. Instead, the method employs a set of local approximate solutions of flow to impose continuity of hydraulic head and Darcy fluxes throughout space and time. Given sufficient (but limited) measurements, it yields well-posed systems of nonlinear equations that can be solved efficiently with optimization. Solution of the inversion includes parameters (hydraulic conductivities, specific storage coefficients) and flow field including the unknown IC and BC. Given error-free measurements, the estimated conductivities and specific storages are accurate within 10% of the true values. When increasing measurement errors are imposed, the estimated parameters become less accurate, but the inverse solution is still stable, i.e., parameter, IC, and BC estimation remains bounded. For a problem where parameter variation is unknown, highly parameterized inversion can reveal the underlying parameter structure, whereas equivalent conductivity and average storage coefficient can also be estimated. Because of the physically-based constraints placed in inversion, the number of measurements does not need to exceed the number of parameters for the inverse method to succeed.

Inverse electrocardiographic source localization of ischemia: An optimization framework and finite element solution

With the goal of non-invasively localizing cardiac ischemic disease using body-surface potential recordings, we attempted to reconstruct the transmembrane potential (TMP) throughout the myocardium with the bidomain heart model. The task is an inverse source problem governed by partial differential equations (PDE). Our main contribution is solving the inverse problem within a PDE-constrained optimization framework that enables various physically-based constraints in both equality and inequality forms. We formulated the optimality conditions rigorously in the continuum before deriving finite element discretization, thereby making the optimization independent of discretization choice. Such a formulation was derived for the L2-norm Tikhonov regularization and the total variation minimization. The subsequent numerical optimization was fulfilled by a primal–dual interior-point method tailored to our problem’s specific structure. Our simulations used realistic, fiber-included heart models consisting of up to 18,000 nodes, much finer than any inverse models previously reported. With synthetic ischemia data we localized ischemic regions with roughly a 10% false-negative rate or a 20% false-positive rate under conditions up to 5% input noise. With ischemia data measured from animal experiments, we reconstructed TMPs with roughly 0.9 correlation with the ground truth. While precisely estimating the TMP in general cases remains an open problem, our study shows the feasibility of reconstructing TMP during the ST interval as a means of ischemia localization.

Extracting motion data from video using optical flow with physically-based constraints

Motion analysis of video data is a powerful tool for studying dynamic behavior and determining sources of failures. In the case of failure analysis, the available video may be of poor quality, such as from surveillance cameras. It is also likely to have been taken from a bad angle, with poor lighting, and occlusions may be present. To address such cases, this paper presents an optical flow-based tracking algorithm incorporating physically-based constraints to extract motion data from video. The technique can accurately track a significant number of data points with a high degree of automation and efficiency. Many traditional methods of video data extraction from poor-quality video have proven tedious and time-consuming due to extensive user-input requirements. With this in mind, the proposed optical flow-based algorithm functions with a minimal degree of user involvement. Points identified at the outset of a video sequence, and within a small subset of frames spaced throughout, can be automatically tracked even when they become occluded or undergo translational, rotational, or deformational motion. The proposed algorithm improves upon previous optical flow-based tracking algorithms by providing greater flexibility and robustness. Example results are presented that show the method tracking machines with flexible components, Segway personal transporters, and athletes pole vaulting.

Physically guided liquid surface modeling from videos

We present an image-based reconstruction framework to model real water scenes captured by stereoscopic video. In contrast to many image-based modeling techniques that rely on user interaction to obtain high-quality 3D models, we instead apply automatically calculated physically-based constraints to refine the initial model. The combination of image-based reconstruction with physically-based simulation allows us to model complex and dynamic objects such as fluid. Using a depth map sequence as initial conditions, we use a physically based approach that automatically fills in missing regions, removes outliers, and refines the geometric shape so that the final 3D model is consistent to both the input video data and the laws of physics. Physically-guided modeling also makes interpolation or extrapolation in the space-time domain possible, and even allows the fusion of depth maps that were taken at different times or viewpoints. We demonstrated the effectiveness of our framework with a number of real scenes, all captured using only a single pair of cameras.

Investigation of molar volume and surfactant characteristics of water-soluble organic compounds in biomass burning aerosol

Abstract. In this study, we characterize the CCN activity of the water-soluble organics in biomass burning aerosol. The aerosol after collection upon filters is dissolved in water using sonication. Hydrophobic and hydrophilic components are fractionated from a portion of the original sample using solid phase extraction, and subsequently desalted. The surface tension and CCN activity of these different samples are measured with a KSV CAM 200 goniometer and a DMT Streamwise Thermal Gradient CCN Counter, respectively. The measurements show that the strongest surfactants are isolated in the hydrophobic fraction, while the hydrophilics exhibit negligible surface tension depression. The presence of salts (primarily (NH4)2SO4) in the hydrophobic fraction substantially enhances surface tension depression; their synergistic effects considerably enhance CCN activity, exceeding that of pure (NH4)2SO4. From our analysis, average thermodynamic properties (i.e, molar volume) are determined for samples using our newly developed Köhler Theory Analysis (KTA) method. The molar mass of the hydrophilic and hydrophobic aerosol components is estimated to be 87±26 g mol−1 and 780±231 g mol−1, respectively. KTA also suggests that the relative proportion (in moles) of hydrophobic to hydrophilic compounds in the original sample to be 1:3. For the first time, KTA is applied to an aerosol with this level of complexity and displays its potential for providing physically-based constraints for GCM parameterizations of the aerosol indirect effect.

Inferring thermodynamic properties from CCN activation experiments: single-component and binary aerosols

Abstract. This study presents a new method, Köhler Theory Analysis (KTA), to infer the molar volume and solubility of organic aerosol constituents. The method is based on measurements of surface tension, chemical composition, and CCN activity coupled with Köhler theory. KTA is evaluated by inferring the molar volume of six known organics (four dicarboxylic acids, one amino acid, and one sugar) in pure form and in mixtures with ammonium sulfate ((NH4)2SO4). The average error in inferred molar volumes are to within 18% of their expected value for organic fractions between 50 and 90%. This suggests that KTA is a potentially powerful tool for determining the CCN characteristic of ambient water soluble organic carbon (WSOC), providing physically-based constraints for aerosol-cloud interaction parameterizations.