123 citations · 146 across the 20 of their papers we have counts for
20 papers
A Paradigm Shift: Fully End-to-End Training for Temporal Sentence Grounding in Videos
Allen He, Qi Liu, Kun Liu +2
Temporal sentence grounding in videos (TSGV) aims to localize a temporal segment that semantically corresponds to a sentence query from an untrimmed video. Most current methods ado…
HOIGen-1M: A Large-scale Dataset for Human-Object Interaction Video Generation
Kun Liu, Qi Liu, Xinchen Liu +5
Text-to-video (T2V) generation has made tremendous progress in generating complicated scenes based on texts. However, human-object interaction (HOI) often cannot be precisely gener…
OmniPrism: Learning Disentangled Visual Concept for Image Generation
Yangyang Li, Daqing Liu, Wu Liu +4
Creative visual concept generation often draws inspiration from specific concepts in a reference image to produce relevant outcomes. However, existing methods are typically constra…
It Takes Two: Accurate Gait Recognition in the Wild via Cross-granularity Alignment
Jinkai Zheng, Xinchen Liu, Boyue Zhang +4
Existing studies for gait recognition primarily utilized sequences of either binary silhouette or human parsing to encode the shapes and dynamics of persons during walking. Silhoue…
Motion Capture from Inertial and Vision Sensors
Xiaodong Chen, Wu Liu, Qian Bao +4
Human motion capture is the foundation for many computer vision and graphics tasks. While industrial motion capture systems with complex camera arrays or expensive wearable sensors…
HumanNeRF-SE: A Simple yet Effective Approach to Animate HumanNeRF with Diverse Poses
Caoyuan Ma, Yu-Lun Liu, Zhixiang Wang +3
We present HumanNeRF-SE, a simple yet effective method that synthesizes diverse novel pose images with simple input. Previous HumanNeRF works require a large number of optimizable…
SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action Segmentation
Qi Liu, Xinchen Liu, Kun Liu +2
Multi-modal human action segmentation is a critical and challenging task with a wide range of applications. Nowadays, the majority of approaches concentrate on the fusion of dense…
Parsing is All You Need for Accurate Gait Recognition in the Wild
Jinkai Zheng, Xinchen Liu, Shuai Wang +3
Binary silhouettes and keypoint-based skeletons have dominated human gait recognition studies for decades since they are easy to extract from video frames. Despite their success in…
Delving into the Frequency: Temporally Consistent Human Motion Transfer in the Fourier Space
Guang Yang, Wu Liu, Xinchen Liu +3
Human motion transfer refers to synthesizing photo-realistic and temporally coherent videos that enable one person to imitate the motion of others. However, current synthetic video…
REMOT: A Region-to-Whole Framework for Realistic Human Motion Transfer
Quanwei Yang, Xinchen Liu, Wu Liu +4
Human Video Motion Transfer (HVMT) aims to, given an image of a source person, generate his/her video that imitates the motion of the driving person. Existing methods for HVMT main…
MAPLE: Masked Pseudo-Labeling autoEncoder for Semi-supervised Point Cloud Action Recognition
Xiaodong Chen, Wu Liu, Xinchen Liu +3
Recognizing human actions from point cloud videos has attracted tremendous attention from both academia and industry due to its wide applications like automatic driving, robotics,…
Gait Recognition in the Wild with Multi-hop Temporal Switch
Jinkai Zheng, Xinchen Liu, Xiaoyan Gu +5
Existing studies for gait recognition are dominated by in-the-lab scenarios. Since people live in real-world senses, gait recognition in the wild is a more practical problem that h…
Gait Recognition in the Wild with Dense 3D Representations and A Benchmark
Jinkai Zheng, Xinchen Liu, Wu Liu +3
Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. However, humans live and walk in…
Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework
Xiaodong Chen, Xinchen Liu, Wu Liu +4
Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimed…
A Baseline Framework for Part-level Action Parsing and Action Recognition
Xiaodong Chen, Xinchen Liu, Kun Liu +2
This technical report introduces our 2nd place solution to Kinetics-TPS Track on Part-level Action Parsing in ICCV DeeperAction Workshop 2021. Our entry is mainly based on YOLOF fo…
Explainable Person Re-Identification with Attribute-guided Metric Distillation
Xiaodong Chen, Xinchen Liu, Wu Liu +3
Despite the great progress of person re-identification (ReID) with the adoption of Convolutional Neural Networks, current ReID models are opaque and only outputs a scalar distance…
TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition
Jinkai Zheng, Xinchen Liu, Chenggang Yan +4
Gait, i.e., the movement pattern of human limbs during locomotion, is a promising biometric for the identification of persons. Despite significant improvement in gait recognition w…
FastReID: A Pytorch Toolbox for General Instance Re-identification
Lingxiao He, Xingyu Liao, Wu Liu +3
General Instance Re-identification is a very important task in the computer vision, which can be widely used in many practical applications, such as person/vehicle re-identificatio…
Multi-Granularity Reasoning for Social Relation Recognition from Images
Meng Zhang, Xinchen Liu, Wu Liu +3
Discovering social relations in images can make machines better interpret the behavior of human beings. However, automatically recognizing social relations in images is a challengi…
PVSS: A Progressive Vehicle Search System for Video Surveillance Networks
Xinchen Liu, Wu Liu, Huadong Ma +1
This paper is focused on the task of searching for a specific vehicle that appeared in the surveillance networks. Existing methods usually assume the vehicle images are well croppe…