53 citations · 111 across the 6 of their papers we have counts for
6 papers
Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks
Xiaoyu Pan, Jiaming Mai, Xinwei Jiang +6
We present a learning algorithm that uses bone-driven motion networks to predict the deformation of loose-fitting garment meshes at interactive rates. Given a garment, we generate…
Pose Guided Image Generation from Misaligned Sources via Residual Flow Based Correction
Jiawei Lu, He Wang, Tianjia Shao +2
Generating new images with desired properties (e.g. new view/poses) from source images has been enthusiastically pursued recently, due to its wide range of potential applications.…
Unsupervised Image Generation with Infinite Generative Adversarial Networks
Hui Ying, He Wang, Tianjia Shao +2
Image generation has been heavily investigated in computer vision, where one core research challenge is to generate images from arbitrarily complex distributions with little superv…
High-order Differentiable Autoencoder for Nonlinear Model Reduction
Siyuan Shen, Yang Yin, Tianjia Shao +4
This paper provides a new avenue for exploiting deep neural networks to improve physics-based simulation. Specifically, we integrate the classic Lagrangian mechanics with a deep au…
SMART: Skeletal Motion Action Recognition aTtack
He Wang, Feixiang He, Zhexi Peng +4
Adversarial attack has inspired great interest in computer vision, by showing that classification-based solutions are prone to imperceptible attack in many tasks. In this paper, we…
DeepWarp: DNN-based Nonlinear Deformation
Ran Luo, Tianjia Shao, Huamin Wang +3
DeepWarp is an efficient and highly re-usable deep neural network (DNN) based nonlinear deformable simulation framework. Unlike other deep learning applications such as image recog…