View-dependent adaptive cloth simulation

This paper describes a method for view-dependent cloth simulation using dynamically adaptive mesh refinement and coarsening. Given a prescribed camera motion, the method adjusts the criteria controlling refinement to account for visibility and apparent size in the camera's view. Objectionable dynamic artifacts are avoided by anticipative refinement and smoothed coarsening. This approach preserves the appearance of detailed cloth throughout the animation while avoiding the wasted effort of simulating details that would not be discernible to the viewer. The computational savings realized by this method increase as scene complexity grows, producing a 2× speed-up for a single character and more than 4× for a small group

Drape simulation using solid-shell elements and adaptive mesh subdivision

In this paper, 4-node quadrilateral and 3-node triangular solid-shell elements are applied to drape simulations. With locking issues alleviated by the assumed natural strain method and plane-stress enforcement, static and dynamic drape problems are attempted by the quadrilateral element. If the drape is deep and the mesh density is inadequate, non-realistic sharp folds are predicted due to the non-physical interpenetration of top and bottom element surfaces. To avoid the interpenetration, a reversible adaptive subdivision based on the 1–4 splitting method is developed. To ensure displacement compatibility among elements at different subdivision levels, macro-transition elements are formed by quadrilateral and triangular solid-shell elements. To reduce the dynamic oscillation induced by newly inserted nodes, the discrete Kirchhoff condition is employed to determine the related nodal variables. Dynamic drape examples using adaptive meshing are presented. It can be seen that the predictions look realistic and deep drapes can be predicted with the interpenetration avoided yet the required number of nodes can be kept relatively small.postprin

Interactive procedural simulation of paper tearing with sound

International audienceWe present a phenomenological model for the real-time simulation of paper tearing and sound. The model uses as input rotations of the hand along with the index and thumb of left and right hands to drive the position and orientation of two regions of a sheet of paper. The motion of the hands produces a cone shaped deformation of the paper and guides the formation and growth of the tear. We create a model for the direction of the tear based on empirical observation, and add detail to the tear with a directed noise model. Furthermore, we present a procedural sound synthesis method to produce tearing sounds during interaction. We show a variety of paper tearing examples and discuss applications and limitations

Learning an Intrinsic Garment Space for Interactive Authoring of Garment Animation

Authoring dynamic garment shapes for character animation on body motion is one of the fundamental steps in the CG industry. Established workflows are either time and labor consuming (i.e., manual editing on dense frames with controllers), or lack keyframe-level control (i.e., physically-based simulation). Not surprisingly, garment authoring remains a bottleneck in many production pipelines. Instead, we present a deep-learning-based approach for semi-automatic authoring of garment animation, wherein the user provides the desired garment shape in a selection of keyframes, while our system infers a latent representation for its motion-independent intrinsic parameters (e.g., gravity, cloth materials, etc.). Given new character motions, the latent representation allows to automatically generate a plausible garment animation at interactive rates. Having factored out character motion, the learned intrinsic garment space enables smooth transition between keyframes on a new motion sequence. Technically, we learn an intrinsic garment space with an motion-driven autoencoder network, where the encoder maps the garment shapes to the intrinsic space under the condition of body motions, while the decoder acts as a differentiable simulator to generate garment shapes according to changes in character body motion and intrinsic parameters. We evaluate our approach qualitatively and quantitatively on common garment types. Experiments demonstrate our system can significantly improve current garment authoring workflows via an interactive user interface. Compared with the standard CG pipeline, our system significantly reduces the ratio of required keyframes from 20% to 1 -- 2%

Non-smooth developable geometry for interactively animating paper crumpling

International audienceWe present the first method to animate sheets of paper at interactive rates, while automatically generating a plausible set of sharp features when the sheet is crumpled. The key idea is to interleave standard physically-based simulation steps with procedural generation of a piecewise continuous developable surface. The resulting hybrid surface model captures new singular points dynamically appearing during the crumpling process, mimicking the effect of paper fiber fracture. Although the model evolves over time to take these irreversible damages into account, the mesh used for simulation is kept coarse throughout the animation, leading to efficient computations. Meanwhile, the geometric layer ensures that the surface stays almost isometric to its original 2D pattern. We validate our model through measurements and visual comparison with real paper manipulation, and show results on a variety of crumpled paper configurations

Real-time Error Control for Surgical Simulation

Objective: To present the first real-time a posteriori error-driven adaptive finite element approach for real-time simulation and to demonstrate the method on a needle insertion problem. Methods: We use corotational elasticity and a frictional needle/tissue interaction model. The problem is solved using finite elements within SOFA. The refinement strategy relies upon a hexahedron-based finite element method, combined with a posteriori error estimation driven local $h$-refinement, for simulating soft tissue deformation. Results: We control the local and global error level in the mechanical fields (e.g. displacement or stresses) during the simulation. We show the convergence of the algorithm on academic examples, and demonstrate its practical usability on a percutaneous procedure involving needle insertion in a liver. For the latter case, we compare the force displacement curves obtained from the proposed adaptive algorithm with that obtained from a uniform refinement approach. Conclusions: Error control guarantees that a tolerable error level is not exceeded during the simulations. Local mesh refinement accelerates simulations. Significance: Our work provides a first step to discriminate between discretization error and modeling error by providing a robust quantification of discretization error during simulations.Comment: 12 pages, 16 figures, change of the title, submitted to IEEE TBM

NeuralClothSim: Neural Deformation Fields Meet the Kirchhoff-Love Thin Shell Theory

Cloth simulation is an extensively studied problem, with a plethora of solutions available in computer graphics literature. Existing cloth simulators produce realistic cloth deformations that obey different types of boundary conditions. Nevertheless, their operational principle remains limited in several ways: They operate on explicit surface representations with a fixed spatial resolution, perform a series of discretised updates (which bounds their temporal resolution), and require comparably large amounts of storage. Moreover, back-propagating gradients through the existing solvers is often not straightforward, which poses additional challenges when integrating them into modern neural architectures. In response to the limitations mentioned above, this paper takes a fundamentally different perspective on physically-plausible cloth simulation and re-thinks this long-standing problem: We propose NeuralClothSim, i.e., a new cloth simulation approach using thin shells, in which surface evolution is encoded in neural network weights. Our memory-efficient and differentiable solver operates on a new continuous coordinate-based representation of dynamic surfaces, i.e., neural deformation fields (NDFs); it supervises NDF evolution with the rules of the non-linear Kirchhoff-Love shell theory. NDFs are adaptive in the sense that they 1) allocate their capacity to the deformation details as the latter arise during the cloth evolution and 2) allow surface state queries at arbitrary spatial and temporal resolutions without retraining. We show how to train our NeuralClothSim solver while imposing hard boundary conditions and demonstrate multiple applications, such as material interpolation and simulation editing. The experimental results highlight the effectiveness of our formulation and its potential impact.Comment: 27 pages, 22 figures and 3 tables; project page: https://4dqv.mpi-inf.mpg.de/NeuralClothSim

Multifarious Hierarchies of Mechanical Models for Artist Assigned Levels-of-Detail

International audienceWe present a new framework for artist driven level of detail in solid simulations. Simulated objects are simultaneously embedded in several, separately designed deformation models with their own independent degrees of freedom. The models are ordered to apply their deformations hierarchically, and we enforce the uniqueness of the dynamics solutions using a novel kinetic filtering operator designed to ensure that each child only adds detail motion to its parent without introducing redundancies. This new approach allows artists to easily add fine-scale details without introducing unnecessary degrees-of-freedom to the simulation or resorting to complex geometric operations like anisotropic volume meshing. We illustrate the utility of our approach with several detail enriched simulation examples