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Found 25 papers, 7 papers with code
GAN-based Garment Generation Using Sewing Pattern Images
The generation of realistic apparel model has become increasingly popular as a result of the rapid pace of change in fashion trends and the growing need for garment models in various applications such as virtual try-on.
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.
Gradient-based Trajectory Optimization with Parallelized Differentiable Traffic Simulation
We show that we can use the simulator to filter noise in the input trajectories (trajectory filtering), reconstruct dense trajectories from sparse ones (trajectory reconstruction), and predict future trajectories (trajectory prediction), with all generated trajectories adhering to physical laws.
Differentiable Quantum Computing for Large-scale Linear Control
Compared to the classical approaches, our method achieves a super-quadratic speedup.
3D-free meets 3D priors: Novel View Synthesis from a Single Image with Pretrained Diffusion Guidance
Recent 3D novel view synthesis (NVS) methods often require extensive 3D data for training, and also typically lack generalization beyond the training distribution.
An Intrinsic Vector Heat Network
Vector fields are widely used to represent and model flows for many science and engineering applications.
Sensitivity-Informed Augmentation for Robust Segmentation
Then, we model the sensitivity curve using the adaptive SA and sample perturbation hyperparameter values accordingly.
DMesh: A Differentiable Mesh Representation
We present a differentiable representation, DMesh, for general 3D triangular meshes.
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.
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.
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.
Human Body Measurement Estimation with Adversarial Augmentation
Training of BMnet is performed on data from real human subjects, and augmented with a novel adversarial body simulator (ABS) that finds and synthesizes challenging body shapes.
Differentiable Simulation of Soft Multi-body Systems
We present a method for differentiable simulation of soft articulated bodies.
Voice Aging with Audio-Visual Style Transfer
Face aging techniques have used generative adversarial networks (GANs) and style transfer learning to transform one's appearance to look younger/older.
Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction
Reflective and textureless surfaces such as windows, mirrors, and walls can be a challenge for object and scene reconstruction.
3D-MOV: Audio-Visual LSTM Autoencoder for 3D Reconstruction of Multiple Objects from Video
Our neural network produces high-quality 3D reconstructions using voxel representation.
Efficient Differentiable Simulation of Articulated Bodies
We derive the gradients of the forward dynamics using spatial algebra and the adjoint method.
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images
For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.
Scalable Differentiable Physics for Learning and Control
Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments.
Differentiable Physics Simulation
Differentiable physics simulation is a powerful family of new techniques that applies gradient-based methods to learning and control of physical systems.
Shape-Aware Human Pose and Shape Reconstruction Using Multi-View Images
We propose a scalable neural network framework to reconstruct the 3D mesh of a human body from multi-view images, in the subspace of the SMPL model.
Ranked #4 on
Multi-view 3D Human Pose Estimation
on MPI-INF-3DHP
3D Human Pose Estimation
Multi-view 3D Human Pose Estimation
ISNN: Impact Sound Neural Network for Audio-Visual Object Classification
3D object geometry reconstruction remains a challenge when working with transparent, occluded, or highly reflective surfaces.
Learning-Based Cloth Material Recovery From Video
To extract information about the cloth, our method characterizes both the motion space and the visual appearance of the cloth geometry.
Detailed Garment Recovery from a Single-View Image
Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs from the web.
