Search Results for author: Ming C. Lin

Found 19 papers, 5 papers with code

GAN-based Garment Generation Using Sewing Pattern Images

no code implementations ECCV 2020 Yu Shen, Junbang Liang, Ming C. Lin

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.

Garment Reconstruction Virtual Try-on

DMesh: A Differentiable Representation for General Meshes

no code implementations20 Apr 2024 Sanghyun Son, Matheus Gadelha, Yang Zhou, Zexiang Xu, Ming C. Lin, Yi Zhou

We present a differentiable representation, DMesh, for general 3D triangular meshes.

Gradient Informed Proximal Policy Optimization

1 code implementation NeurIPS 2023 Sanghyun Son, Laura Yu Zheng, Ryan Sullivan, Yi-Ling Qiao, Ming C. Lin

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

1 code implementation ICCV 2023 Yi-Ling Qiao, Alexander Gao, Yiran Xu, Yue Feng, Jia-Bin Huang, Ming C. Lin

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

no code implementations22 Oct 2022 Yi-Ling Qiao, Alexander Gao, Ming C. Lin

We present a method for learning 3D geometry and physics parameters of a dynamic scene from only a monocular RGB video input.

Video Reconstruction

Differentiable Hybrid Traffic Simulation

no code implementations14 Oct 2022 Sanghyun Son, Yi-Ling Qiao, Jason Sewall, Ming C. Lin

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

no code implementations11 Oct 2022 Nataniel Ruiz, Miriam Bellver, Timo Bolkart, Ambuj Arora, Ming C. Lin, Javier Romero, Raja Bala

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.

Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction

no code implementations5 Oct 2021 Justin Wilson, Nicholas Rewkowski, Ming C. Lin, Henry Fuchs

Reflective and textureless surfaces such as windows, mirrors, and walls can be a challenge for object and scene reconstruction.

3D Reconstruction 3D Scene Reconstruction +3

Voice Aging with Audio-Visual Style Transfer

no code implementations5 Oct 2021 Justin Wilson, Sunyeong Park, Seunghye J. Wilson, Ming C. Lin

Face aging techniques have used generative adversarial networks (GANs) and style transfer learning to transform one's appearance to look younger/older.

Style Transfer Transfer Learning

Efficient Differentiable Simulation of Articulated Bodies

3 code implementations16 Sep 2021 Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin

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

no code implementations26 Feb 2021 Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin

For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.

Autonomous Driving Data Augmentation +1

Scalable Differentiable Physics for Learning and Control

3 code implementations ICML 2020 Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin

Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments.

Differentiable Physics Simulation

no code implementations ICLR Workshop DeepDiffEq 2019 Junbang Liang, Ming C. Lin

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

no code implementations ICCV 2019 Junbang Liang, Ming C. Lin

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.

3D Human Pose Estimation

Learning-Based Cloth Material Recovery From Video

no code implementations ICCV 2017 Shan Yang, Junbang Liang, Ming C. Lin

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

no code implementations3 Aug 2016 Shan Yang, Tanya Ambert, Zherong Pan, Ke Wang, Licheng Yu, Tamara Berg, Ming C. Lin

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.

Semantic Parsing Virtual Try-on

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