Search Results for author: Minglun Gong

Found 25 papers, 6 papers with code

CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP

no code implementations15 Mar 2022 Mingjie Wang, Jun Zhou, Hao Cai, Minglun Gong

Existing state-of-the-art crowd counting algorithms rely excessively on location-level annotations, which are burdensome to acquire.

Crowd Counting

3D Pose Estimation and Future Motion Prediction from 2D Images

no code implementations26 Nov 2021 Ji Yang, Youdong Ma, Xinxin Zuo, Sen Wang, Minglun Gong, Li Cheng

This paper considers to jointly tackle the highly correlated tasks of estimating 3D human body poses and predicting future 3D motions from RGB image sequences.

3D Pose Estimation motion prediction

Action2video: Generating Videos of Human 3D Actions

no code implementations12 Nov 2021 Chuan Guo, Xinxin Zuo, Sen Wang, Xinshuang Liu, Shihao Zou, Minglun Gong, Li Cheng

Action2motion stochastically generates plausible 3D pose sequences of a prescribed action category, which are processed and rendered by motion2video to form 2D videos.

EventHPE: Event-based 3D Human Pose and Shape Estimation

1 code implementation ICCV 2021 Shihao Zou, Chuan Guo, Xinxin Zuo, Sen Wang, Pengyu Wang, Xiaoqin Hu, Shoushun Chen, Minglun Gong, Li Cheng

Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals.

3D human pose and shape estimation Optical Flow Estimation

Self-supervised 3D Human Mesh Recovery from Noisy Point Clouds

1 code implementation15 Jul 2021 Xinxin Zuo, Sen Wang, Qiang Sun, Minglun Gong, Li Cheng

However, Chamfer distance is quite sensitive to noise and outliers, thus could be unreliable to assign correspondences.

Human Mesh Recovery

STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting

no code implementations18 Dec 2020 Mingjie Wang, Hao Cai, XianFeng Han, Jun Zhou, Minglun Gong

To battle the ingrained issue of accuracy degradation, we propose a novel and powerful network called Scale Tree Network (STNet) for accurate crowd counting.

Crowd Counting

TAP-Net: Transport-and-Pack using Reinforcement Learning

no code implementations3 Sep 2020 Ruizhen Hu, Juzhan Xu, Bin Chen, Minglun Gong, Hao Zhang, Hui Huang

Using a learning-based approach, a trained network can learn and encode solution patterns to guide the solution of new problem instances instead of executing an expensive online search.

reinforcement-learning

Action2Motion: Conditioned Generation of 3D Human Motions

1 code implementation30 Jul 2020 Chuan Guo, Xinxin Zuo, Sen Wang, Shihao Zou, Qingyao Sun, Annan Deng, Minglun Gong, Li Cheng

Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category.

Action Generation

3D Human Shape Reconstruction from a Polarization Image

no code implementations ECCV 2020 Shihao Zou, Xinxin Zuo, Yiming Qian, Sen Wang, Chi Xu, Minglun Gong, Li Cheng

Inspired by the recent advances in human shape estimation from single color images, in this paper, we attempt at estimating human body shapes by leveraging the geometric cues from single polarization images.

SparseFusion: Dynamic Human Avatar Modeling from Sparse RGBD Images

no code implementations5 Jun 2020 Xinxin Zuo, Sen Wang, Jiangbin Zheng, Weiwei Yu, Minglun Gong, Ruigang Yang, Li Cheng

First, based on a generative human template, for every two frames having sufficient overlap, an initial pairwise alignment is performed; It is followed by a global non-rigid registration procedure, in which partial results from RGBD frames are collected into a unified 3D shape, under the guidance of correspondences from the pairwise alignment; Finally, the texture map of the reconstructed human model is optimized to deliver a clear and spatially consistent texture.

Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting

no code implementations25 May 2020 Mingjie Wang, Hao Cai, Jun Zhou, Minglun Gong

Crowd counting is an important vision task, which faces challenges on continuous scale variation within a given scene and huge density shift both within and across images.

Crowd Counting

Polarization Human Shape and Pose Dataset

no code implementations30 Apr 2020 Shihao Zou, Xinxin Zuo, Yiming Qian, Sen Wang, Chuan Guo, Chi Xu, Minglun Gong, Li Cheng

Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing detailed surface normal of the objects of interest.

ETNet: Error Transition Network for Arbitrary Style Transfer

1 code implementation NeurIPS 2019 Chunjin Song, Zhijie Wu, Yang Zhou, Minglun Gong, Hui Huang

Numerous valuable efforts have been devoted to achieving arbitrary style transfer since the seminal work of Gatys et al.

Style Transfer

Multi-scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets

no code implementations2 Oct 2018 Mingjie Wang, Jun Zhou, Wendong Mao, Minglun Gong

To address this problem, a regularization method named Stochastic Feature Reuse is also presented.

Simultaneous 3D Reconstruction for Water Surface and Underwater Scene

no code implementations ECCV 2018 Yiming Qian, Yinqiang Zheng, Minglun Gong, Yee-Hong Yang

This paper presents the first approach for simultaneously recovering the 3D shape of both the wavy water surface and the moving underwater scene.

3D Reconstruction Optical Flow Estimation

Full 3D Reconstruction of Transparent Objects

no code implementations9 May 2018 Bojian Wu, Yang Zhou, Yiming Qian, Minglun Gong, Hui Huang

Numerous techniques have been proposed for reconstructing 3D models for opaque objects in past decades.

3D Reconstruction Transparent objects

BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis

2 code implementations22 Mar 2018 Zili Yi, Zhiqin Chen, Hao Cai, Wendong Mao, Minglun Gong, Hao Zhang

The key feature of BSD-GAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features.

Image Generation Representation Learning

Frequency-Based Environment Matting by Compressive Sensing

no code implementations ICCV 2015 Yiming Qian, Minglun Gong, Yee-Hong Yang

Extracting environment mattes using existing approaches often requires either thousands of captured images or a long processing time, or both.

Compressive Sensing Image Matting

Underwater Camera Calibration Using Wavelength Triangulation

no code implementations CVPR 2013 Timothy Yau, Minglun Gong, Yee-Hong Yang

In underwater imagery, the image formation process includes refractions that occur when light passes from water into the camera housing, typically through a flat glass port.

Camera Calibration

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