Search Results for author:
Found 15 papers, 5 papers with code
PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF
We show that physics-based simulations can be seamlessly integrated with NeRF to generate high-quality elastodynamics of real-world objects.
PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics
We introduce PhysGaussian, a new method that seamlessly integrates physically grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis.
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture
We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture.
PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology.
A Reconfigurable Data Glove for Reconstructing Physical and Virtual Grasps
In this work, we present a reconfigurable data glove design to capture different modes of human hand-object interactions, which are critical in training embodied artificial intelligence (AI) agents for fine manipulation tasks.
TPA-Net: Generate A Dataset for Text to Physics-based Animation
Recent breakthroughs in Vision-Language (V&L) joint research have achieved remarkable results in various text-driven tasks.
Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN
Bi-stride pools nodes on every other frontier of the breadth-first search (BFS), without the need for the manual drawing of coarser meshes and avoiding the wrong edges by spatial proximity.
Complex Locomotion Skill Learning via Differentiable Physics
Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers.
High-order Differentiable Autoencoder for Nonlinear Model Reduction
This paper provides a new avenue for exploiting deep neural networks to improve physics-based simulation.
Second-order Neural Network Training Using Complex-step Directional Derivative
We believe our method will inspire a wide-range of new algorithms for deep learning and numerical optimization.
Lagrangian-Eulerian Multi-Density Topology Optimization with the Material Point Method
A quadrature-level connectivity graph-based method is adopted to avoid the artificial checkerboard issues commonly existing in multi-resolution topology optimization methods.
Computational Physics Computational Engineering, Finance, and Science Graphics
Hierarchical Optimization Time Integration for CFL-rate MPM Stepping
We propose Hierarchical Optimization Time Integration (HOT) for efficient implicit time-stepping of the Material Point Method (MPM) irrespective of simulated materials and conditions.
Graphics
Human-centric Indoor Scene Synthesis Using Stochastic Grammar
We present a human-centric method to sample and synthesize 3D room layouts and 2D images thereof, to obtain large-scale 2D/3D image data with perfect per-pixel ground truth.
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars
We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms.
Inferring Forces and Learning Human Utilities From Videos
We propose a notion of affordance that takes into account physical quantities generated when the human body interacts with real-world objects, and introduce a learning framework that incorporates the concept of human utilities, which in our opinion provides a deeper and finer-grained account not only of object affordance but also of people's interaction with objects.
