Search Results for author:
Found 25 papers, 6 papers with code
CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP
Existing state-of-the-art crowd counting algorithms rely excessively on location-level annotations, which are burdensome to acquire.
3D Pose Estimation and Future Motion Prediction from 2D Images
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.
Action2video: Generating Videos of Human 3D Actions
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
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.
Object Wake-up: 3D Object Reconstruction and Animation from a Single Image
Given a single chair image, could we extract its 3D shape and animate its plausible articulations and motions?
Self-supervised 3D Human Mesh Recovery from Noisy Point Clouds
However, Chamfer distance is quite sensitive to noise and outliers, thus could be unreliable to assign correspondences.
STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting
To battle the ingrained issue of accuracy degradation, we propose a novel and powerful network called Scale Tree Network (STNet) for accurate crowd counting.
TAP-Net: Transport-and-Pack using Reinforcement Learning
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.
Action2Motion: Conditioned Generation of 3D Human Motions
Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category.
3D Human Shape Reconstruction from a Polarization Image
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
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
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.
Polarization Human Shape and Pose Dataset
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
Numerous valuable efforts have been devoted to achieving arbitrary style transfer since the seminal work of Gatys et al.
EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer
Style transfer has been an important topic both in computer vision and graphics.
Multi-scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets
To address this problem, a regularization method named Stochastic Feature Reuse is also presented.
Semi-dense Stereo Matching using Dual CNNs
A robust solution for semi-dense stereo matching is presented.
Simultaneous 3D Reconstruction for Water Surface and Underwater Scene
This paper presents the first approach for simultaneously recovering the 3D shape of both the wavy water surface and the moving underwater scene.
Full 3D Reconstruction of Transparent Objects
Numerous techniques have been proposed for reconstructing 3D models for opaque objects in past decades.
BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis
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.
Stereo-Based 3D Reconstruction of Dynamic Fluid Surfaces by Global Optimization
3D reconstruction of dynamic fluid surfaces is an open and challenging problem in computer vision.
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN.
Ranked #2 on
Image-to-Image Translation
on Aerial-to-Map
3D Reconstruction of Transparent Objects With Position-Normal Consistency
Estimating the shape of transparent and refractive objects is one of the few open problems in 3D reconstruction.
Frequency-Based Environment Matting by Compressive Sensing
Extracting environment mattes using existing approaches often requires either thousands of captured images or a long processing time, or both.
Underwater Camera Calibration Using Wavelength Triangulation
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.
