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Found 17 papers, 8 papers with code
HumanNeRF-SE: A Simple yet Effective Approach to Animate HumanNeRF with Diverse Poses
Our architecture involving both explicit and implicit representation is simple yet effective.
SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action Segmentation
Nowadays, the majority of approaches concentrate on the fusion of dense signals (i. e., RGB, optical flow, and depth maps).
Parsing is All You Need for Accurate Gait Recognition in the Wild
Furthermore, due to the lack of suitable datasets, we build the first parsing-based dataset for gait recognition in the wild, named Gait3D-Parsing, by extending the large-scale and challenging Gait3D dataset.
Delving into the Frequency: Temporally Consistent Human Motion Transfer in the Fourier Space
To close the frequency gap between the natural and synthetic videos, we propose a novel Frequency-based human MOtion TRansfer framework, named FreMOTR, which can effectively mitigate the spatial artifacts and the temporal inconsistency of the synthesized videos.
REMOT: A Region-to-Whole Framework for Realistic Human Motion Transfer
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.
MAPLE: Masked Pseudo-Labeling autoEncoder for Semi-supervised Point Cloud Action Recognition
In DestFormer, the spatial and temporal dimensions of the 4D point cloud videos are decoupled to achieve efficient self-attention for learning both long-term and short-term features.
Gait Recognition in the Wild with Multi-hop Temporal Switch
Current methods that obtain state-of-the-art performance on in-the-lab benchmarks achieve much worse accuracy on the recently proposed in-the-wild datasets because these methods can hardly model the varied temporal dynamics of gait sequences in unconstrained scenes.
Gait Recognition in the Wild with Dense 3D Representations and A Benchmark
Based on Gait3D, we comprehensively compare our method with existing gait recognition approaches, which reflects the superior performance of our framework and the potential of 3D representations for gait recognition in the wild.
Ranked #1 on
Gait Recognition
on Gait3D
Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework
Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video.
A Baseline Framework for Part-level Action Parsing and Action Recognition
This technical report introduces our 2nd place solution to Kinetics-TPS Track on Part-level Action Parsing in ICCV DeeperAction Workshop 2021.
Explainable Person Re-Identification with Attribute-guided Metric Distillation
In this paper, we propose a post-hoc method, named Attribute-guided Metric Distillation (AMD), to explain existing ReID models.
Ranked #44 on
Person Re-Identification
on DukeMTMC-reID
TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition
Despite significant improvement in gait recognition with deep learning, existing studies still neglect a more practical but challenging scenario -- unsupervised cross-domain gait recognition which aims to learn a model on a labeled dataset then adapts it to an unlabeled dataset.
SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization
The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets.
FastReID: A Pytorch Toolbox for General Instance Re-identification
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-identification, face recognition, wildlife protection, commodity tracing, and snapshop, etc.. To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research.
Ranked #1 on
Person Re-Identification
on MSMT17-C
Social Relation Recognition From Videos via Multi-Scale Spatial-Temporal Reasoning
On one hand, the actions and storylines in videos provide more important cues for social relation recognition.
Multi-Granularity Reasoning for Social Relation Recognition from Images
To bridge the domain gap, we propose a Multi-Granularity Reasoning framework for social relation recognition from images.
Ranked #3 on
Visual Social Relationship Recognition
on PISC
PVSS: A Progressive Vehicle Search System for Video Surveillance Networks
In this paper, a Progressive Vehicle Search System, named as PVSS, is designed to solve the above problems.
