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Item Cosserat Rod with rh-Adaptive Discretization(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wen, Jiahao; Chen, Jiong; Nobuyuki, Umetani; Bao, Hujun; Huang, Jin; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-LueRod-like one-dimensional elastic objects often exhibit complex behaviors which pose great challenges to the discretization method for pursuing a faithful simulation. By only moving a small portion of material points, the Eulerian-on-Lagrangian (EoL) method already shows great adaptivity to handle sharp contact, but it is still far from enough to reproduce rich and complex geometry details arising in simulations. In this paper, we extend the discrete configuration space by unifying all Lagrangian and EoL nodes in representation for even more adaptivity with every sample being assigned with a dynamic material coordinate. However, this great extension will immediately bring in much more redundancy in the dynamic system. Therefore, we propose additional energy to control the spatial distribution of all material points, seeking to equally space them with respect to a curvature-based density field as a monitor. This flexible approach can effectively constrain the motion of material points to resolve numerical degeneracy, while simultaneously enables them to notably slide inside the parametric domain to account for the shape parameterization. Besides, to accurately respond to sharp contact, our method can also insert or remove nodes online and adjust the energy stiffness to suppress possible jittering artifacts that could be excited in a stiff system. As a result of this hybrid rh-adaption, our proposed method is capable of reproducing many realistic rod dynamics, such as excessive bending, twisting and knotting while only using a limited number of elements.Item Reconstructing Baseball Pitching Motions from Video(The Eurographics Association, 2023) Kim, Jiwon; Kim, Dongkwon; Yu, Ri; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.Baseball is one of the most loved sports in the world. In baseball game, the pitcher's control ability is a key factor for determining the outcome of the game. There are a lot of video data shooting baseball games, and learning baseball pitching motions from video can be possible thanks to the pose estimation techniques. However, reconstructing pitching motions using pose estimators is challenging. When we watch a baseball game, motion blur occurs inevitably because the pitcher throws a ball into the strike zone as fast as possible. To tackle this problem, We propose a framework using physics simulation and deep reinforcement learning to reconstruct baseball pitching motions based on unsatisfactory poses estimated from video. We set the target point and design rewards to encourage the character to throw the ball to the target point. Consequently, we can reconstruct plausible pitching motion.Item N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks(The Eurographics Association and John Wiley & Sons Ltd., 2022) Li, Yu Di; Tang, Min; Yang, Yun; Huang, Zi; Tong, Ruo Feng; Yang, Shuang Cai; Li, Yao; Manocha, Dinesh; Chaine, Raphaëlle; Kim, Min H.We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by triangle meshes with arbitrary topologies.We use graph convolution to transform the cloth and object meshes into a latent space to reduce the non-linearity in the mesh space. Our network can predict the target 3D cloth mesh deformation based on the initial state of the cloth mesh template and the target obstacle mesh. Our approach can handle complex cloth meshes with up to 100K triangles and scenes with various objects corresponding to SMPL humans, non-SMPL humans or rigid bodies. In practice, our approach can be used to generate plausible cloth simulation at 30??45 fps on an NVIDIA GeForce RTX 3090 GPU. We highlight its benefits over prior learning-based methods and physicallybased cloth simulators.Item Simulation of Mechanical Weathering for Modeling Rocky Terrains(The Eurographics Association, 2024) Mateos, Diego; Carranza, Luis; Susin, Anton; Argudo, Oscar; Marco, Julio; Patow, GustavoSynthetic terrains play a vital role in various applications, including entertainment, training, and simulation. This work focuses on rocky terrains akin to those found in alpine environments, which contain many complex features such as sharp ridges, loose blocks, or overhangs that are often inadequately represented by standard 2D elevation maps. We propose a novel method based on a simplified simulation of mechanical erosion processes commonly observed in high-altitude terrains, in particular the weathering due to freeze-thaw cycles. The ultimate objective is to generate plausible rocky geometry from existing 3D models, as well as account for the temporal evolution due to these weathering processes. Additionally, we have developed an artist-friendly tool integrated as an add-on into Blender.Item Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wiewel, Steffen; Kim, Byungsoo; Azevedo, Vinicius; Solenthaler, Barbara; Thuerey, Nils; Bender, Jan and Popa, TiberiuWe propose an end-to-end trained neural network architecture to robustly predict the complex dynamics of fluid flows with high temporal stability. We focus on single-phase smoke simulations in 2D and 3D based on the incompressible Navier-Stokes (NS) equations, which are relevant for a wide range of practical problems. To achieve stable predictions for long-term flow sequences with linear execution times, a convolutional neural network (CNN) is trained for spatial compression in combination with a temporal prediction network that consists of stacked Long Short-Term Memory (LSTM) layers. Our core contribution is a novel latent space subdivision (LSS) to separate the respective input quantities into individual parts of the encoded latent space domain. As a result, this allows to distinctively alter the encoded quantities without interfering with the remaining latent space values and hence maximizes external control. By selectively overwriting parts of the predicted latent space points, our proposed method is capable to robustly predict long-term sequences of complex physics problems, like the flow of fluids. In addition, we highlight the benefits of a recurrent training on the latent space creation, which is performed by the spatial compression network. Furthermore, we thoroughly evaluate and discuss several different components of our method.Item Frequency-Aware Reconstruction of Fluid Simulations with Generative Networks(The Eurographics Association, 2020) Biland, Simon; Azevedo, Vinicius C.; Kim, Byungsoo; Solenthaler, Barbara; Wilkie, Alexander and Banterle, FrancescoConvolutional neural networks were recently employed to fully reconstruct fluid simulation data from a set of reduced parameters. However, since (de-)convolutions traditionally trained with supervised l1-loss functions do not discriminate between low and high frequencies in the data, the error is not minimized efficiently for higher bands. This directly correlates with the quality of the perceived results, since missing high frequency details are easily noticeable. In this paper, we analyze the reconstruction quality of generative networks and present a frequency-aware loss function that is able to focus on specific bands of the dataset during training time. We show that our approach improves reconstruction quality of fluid simulation data in mid-frequency bands, yielding perceptually better results while requiring comparable training time.Item Graph Partitioning Algorithms for Rigid Body Simulations(The Eurographics Association, 2022) Liu, Yinchu; Andrews, Sheldon; Pelechano, Nuria; Vanderhaeghe, DavidWe propose several graph partitioning algorithms for improving the performance of rigid body simulations. The algorithms operate on the graph formed by rigid bodies (nodes) and constraints (edges), producing non-overlapping and contiguous sub-systems that can be simulated in parallel by a domain decomposition technique. We demonstrate that certain partitioning algorithms reduce the computational time of the solver, and graph refinement techniques that reduce coupling between sub-systems, such as the Kernighan-Lin and Fiduccia-Mattheyses algorithms, give additional performance improvements.Item Wavelet Potentials: An Efficient Potential Recovery Technique for Pointwise Incompressible Fluids(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lyu, Luan; Ren, Xiaohua; Cao, Wei; Zhu, Jian; Wu, Enhua; Yang, Zhi-Xin; Bermano, Amit H.; Kalogerakis, EvangelosWe introduce an efficient technique for recovering the vector potential in wavelet space to simulate pointwise incompressible fluids. This technique ensures that fluid velocities remain divergence-free at any point within the fluid domain and preserves local volume during the simulation. Divergence-free wavelets are utilized to calculate the wavelet coefficients of the vector potential, resulting in a smooth vector potential with enhanced accuracy, even when the input velocities exhibit some degree of divergence. This enhanced accuracy eliminates the need for additional computational time to achieve a specific accuracy threshold, as fewer iterations are required for the pressure Poisson solver. Additionally, in 3D, since the wavelet transform is taken in-place, only the memory for storing the vector potential is required. These two features make the method remarkably efficient for recovering vector potential for fluid simulation. Furthermore, the method can handle various boundary conditions during the wavelet transform, making it adaptable for simulating fluids with Neumann and Dirichlet boundary conditions. Our approach is highly parallelizable and features a time complexity of O(n), allowing for seamless deployment on GPUs and yielding remarkable computational efficiency. Experiments demonstrate that, taking into account the time consumed by the pressure Poisson solver, the method achieves an approximate 2x speedup on GPUs compared to state-of-the-art vector potential recovery techniques while maintaining a precision level of 10-6 when single float precision is employed. The source code of ’'Wavelet Potentials' can be found in https://github.com/yours321dog/WaveletPotentials.Item Neural Smoke Stylization with Color Transfer(The Eurographics Association, 2020) Christen, Fabienne; Kim, Byungsoo; Azevedo, Vinicius C.; Solenthaler, Barbara; Wilkie, Alexander and Banterle, FrancescoArtistically controlling fluid simulations requires a large amount of manual work by an artist. The recently presented transportbased neural style transfer approach simplifies workflows as it transfers the style of arbitrary input images onto 3D smoke simulations. However, the method only modifies the shape of the fluid but omits color information. In this work, we therefore extend the previous approach to obtain a complete pipeline for transferring shape and color information onto 2D and 3D smoke simulations with neural networks. Our results demonstrate that our method successfully transfers colored style features consistently in space and time to smoke data for different input textures.Item Simulation of Dendritic Painting(The Eurographics Association and John Wiley & Sons Ltd., 2020) Canabal, José A.; Otaduy, Miguel A.; Kim, Byungmoon; Echevarria, Jose; Panozzo, Daniele and Assarsson, UlfWe present a new system for interactive dendritic painting. Dendritic painting is characterized by the unique and intricate branching patterns that grow from the interaction of inks, solvents and medium. Painting sessions thus become very dynamic and experimental. To achieve a compelling simulation of this painting technique we introduce a new Reaction-Diffusion model with carefully designed terms to allow natural interactions in a painting context. We include additional user control not possible in the real world to guide and constrain the growth of the patterns in expressive ways. Our multi-field model is able to capture and simulate all these complex phenomena efficiently in real time, expanding the tools available to the digital artist, while producing compelling animations for motion graphics.