In the field of brittle fracture animation, generating realistic destruction animations using physics-based simulation methods is computationally expensive. While techniques based on Voronoi diagrams or pre-fractured patterns are effective for real-time applications, they fail to incorporate collisi … Continue reading “DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning”
Category: Machine Learning
Video-Based Motion Retargeting Framework between Characters with Various Skeleton Structure
We introduce a motion retargeting framework capable of animating characters with distinct skeletal structures using video data. While prior studies have successfully performed motion retargeting be- tween skeletons with different structures, retargeting noisy and unnatural motion data extracted from … Continue reading “Video-Based Motion Retargeting Framework between Characters with Various Skeleton Structure”
Detail-Aware Deep Clothing Animations Infused with Multi-Source Attributes
This paper presents a novel learning-based clothing deformation method to generate rich and reasonable detailed deformations for garments worn by bodies of various shapes in various animations. In contrast to existing learning-based methods, which require numerous trained models for different garmen … Continue reading “Detail-Aware Deep Clothing Animations Infused with Multi-Source Attributes”
GarMatNet: A Learning-based Method for Predicting 3D Garment Mesh with Parameterized Materials
Recent progress in learning-based methods of garment mesh generation is resulting in increased efficiency and maintenance of reality during the generation process. However, none of the previous works so far have focused on variations in material types based on a parameterized material parameter unde … Continue reading “GarMatNet: A Learning-based Method for Predicting 3D Garment Mesh with Parameterized Materials”
MultiResGNet: Approximating Nonlinear Deformation via Multi-Resolution Graphs
This paper presents a graph-learning-based, powerfully generalized method for automatically generating nonlinear deformation for characters with an arbitrary number of vertices. Large-scale character datasets with a significant number of poses are normally required for training to learn such automat … Continue reading “MultiResGNet: Approximating Nonlinear Deformation via Multi-Resolution Graphs”
A GAN-based Temporally Stable Shading Model for Fast Animation of Photorealistic Hair
We introduce a GAN-based model for shading photorealistic hair animations. This work try to shade photorealistic hairs by extending the unsupervised Generative Adversarial Networks. Also, our model is much faster than the previous onerous rendering algorithms and produces fewer artifacts than other … Continue reading “A GAN-based Temporally Stable Shading Model for Fast Animation of Photorealistic Hair”
Segmentation of Unbalanced and In-homogeneous Point Clouds and Its Application to 3D Scanned Trees
Segmentation of 3D point clouds is still an open issue in the case of unbalanced and in-homogeneous data-sets. In the application context of the modeling of botanical trees, a fundamental challenge consists in separating the leaves from the wood. Based on deep learning and a class decision process, … Continue reading “Segmentation of Unbalanced and In-homogeneous Point Clouds and Its Application to 3D Scanned Trees”
High Accuracy Terrain Reconstruction from Point Clouds Using Implicit Deformable Model
Few previous works have studied the modeling of forest ground surfaces from LiDAR point clouds using implicit functions. Jules et al.’s work [Jules17] is a pioneer in this area. However, by design this approach proposes over-smoothed surfaces, in particular in highly occluded areas, limiting i … Continue reading “High Accuracy Terrain Reconstruction from Point Clouds Using Implicit Deformable Model”
DenseGATs: A Graph-Attention-Based Network for Nonlinear Character Deformation
In animation production, animators always spend significant time and efforts to develop quality deformation systems for characters with complex appearances and details. In order to decrease the time spent repetitively skinning and fine-tuning work, we propose an end-to-end approach to automatically … Continue reading “DenseGATs: A Graph-Attention-Based Network for Nonlinear Character Deformation”
Brittle Fracture Shape Generation of 2D Planes Using Deep Learning
Brittle fracture of plane shape objects, such as glass and concrete, is often seen in the real world. Fracture animation of rigid bodies provides impressive effects by using physics-oriented simulation. However, simulation costs become too high when physics-oriented simulation approaches are chosen … Continue reading “Brittle Fracture Shape Generation of 2D Planes Using Deep Learning”