Search Results for author:
Found 17 papers, 7 papers with code
DMesh++: An Efficient Differentiable Mesh for Complex Shapes
Recent probabilistic methods for 3D triangular meshes capture diverse shapes by differentiable mesh connectivity, but face high computational costs with increased shape details.
Differentiable Quantum Computing for Large-scale Linear Control
Compared to the classical approaches, our method achieves a super-quadratic speedup.
Gradient Informed Proximal Policy Optimization
We introduce a novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization (PPO) algorithm.
HandyPriors: Physically Consistent Perception of Hand-Object Interactions with Differentiable Priors
We demonstrate that HandyPriors attains comparable or superior results in the pose estimation task, and that the differentiable physics module can predict contact information for pose refinement.
Dynamic Mesh-Aware Radiance Fields
Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF) volumes, such that they can be rendered and their dynamics simulated in a physically consistent manner with the NeRF, is under-explored from the system perspective of integrating NeRF into the traditional graphics pipeline.
Towards Generalist Robots: A Promising Paradigm via Generative Simulation
This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots.
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.
Differentiable Analog Quantum Computing for Optimization and Control
We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods.
NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos
We present a method for learning 3D geometry and physics parameters of a dynamic scene from only a monocular RGB video input.
Differentiable Hybrid Traffic Simulation
To compute the gradient flow between two types of traffic models in a hybrid framework, we present a novel intermediate conversion component that bridges the lanes in a differentiable manner as well.
Differentiable Simulation of Soft Multi-body Systems
We present a method for differentiable simulation of soft articulated bodies.
Efficient Differentiable Simulation of Articulated Bodies
We derive the gradients of the forward dynamics using spatial algebra and the adjoint method.
Scalable Differentiable Physics for Learning and Control
Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments.
OF-VO: Efficient Navigation among Pedestrians Using Commodity Sensors
Overall, our OF-VO algorithm using learning-based perception and model-based planning methods offers better performance than prior algorithms in terms of navigation time and success rate of collision avoidance.
Robotics
Learning-based Real-time Detection of Intrinsic Reflectional Symmetry
In this paper, we propose \YL{a} learning-based approach to intrinsic reflectional symmetry detection.
LaplacianNet: Learning on 3D Meshes with Laplacian Encoding and Pooling
3D models are commonly used in computer vision and graphics.
Learning Bidirectional LSTM Networks for Synthesizing 3D Mesh Animation Sequences
In this paper, we present a novel method for learning to synthesize 3D mesh animation sequences with long short-term memory (LSTM) blocks and mesh-based convolutional neural networks (CNNs).
Graphics
