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Found 48 papers, 31 papers with code
SFMViT: SlowFast Meet ViT in Chaotic World
The task of spatiotemporal action localization in chaotic scenes is a challenging task toward advanced video understanding.
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action Recognition
Skeleton-based action recognition has gained considerable traction thanks to its utilization of succinct and robust skeletal representations.
MLP: Motion Label Prior for Temporal Sentence Localization in Untrimmed 3D Human Motions
In this paper, we address the unexplored question of temporal sentence localization in human motions (TSLM), aiming to locate a target moment from a 3D human motion that semantically corresponds to a text query.
Point-In-Context: Understanding Point Cloud via In-Context Learning
With the emergence of large-scale models trained on diverse datasets, in-context learning has emerged as a promising paradigm for multitasking, notably in natural language processing and image processing.
VG4D: Vision-Language Model Goes 4D Video Recognition
By transferring the knowledge of the VLM to the 4D encoder and combining the VLM, our VG4D achieves improved recognition performance.
Identity-aware Dual-constraint Network for Cloth-Changing Person Re-identification
In addition, a Multi-scale Constraint Block (MCB) is designed, which extracts fine-grained identity-related features and effectively transfers cloth-irrelevant knowledge.
Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation
We observe that previous optimization-based methods commonly rely on projection constraint, which only ensures alignment in 2D space, potentially leading to the overfitting problem.
Key-Graph Transformer for Image Restoration
While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, especially with high input resolution.
Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction Recognition
Recognizing interactive actions, including hand-to-hand interaction and human-to-human interaction, has attracted increasing attention for various applications in the field of video analysis and human-robot interaction.
Eye Motion Matters for 3D Face Reconstruction
Recent advances in single-image 3D face reconstruction have shown remarkable progress in various applications.
ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification
Through extensive experiments, we demonstrate that our PointMLS achieves state-of-the-art results on ModelNet-O and competitive results on regular datasets, and it is robust and effective.
Diffusion-based Pose Refinement and Muti-hypothesis Generation for 3D Human Pose Estimaiton
To address these two challenges, we propose a diffusion-based refinement framework called DRPose, which refines the output of deterministic models by reverse diffusion and achieves more suitable multi-hypothesis prediction for the current pose benchmark by multi-step refinement with multiple noises.
Understanding LLMs: A Comprehensive Overview from Training to Inference
Low-cost training and deployment of LLMs represent the future development trend.
Explore Human Parsing Modality for Action Recognition
Multimodal-based action recognition methods have achieved high success using pose and RGB modality.
Ranked #4 on
Action Recognition
on NTU RGB+D 120
Expressive Forecasting of 3D Whole-body Human Motions
Human motion forecasting, with the goal of estimating future human behavior over a period of time, is a fundamental task in many real-world applications.
GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction
The past few years has witnessed the dominance of Graph Convolutional Networks (GCNs) over human motion prediction. Various styles of graph convolutions have been proposed, with each one meticulously designed and incorporated into a carefully-crafted network architecture.
Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning
Under this setting, the model can perceive tasks from prompts and accomplish them without any extra task-specific head predictions or model fine-tuning.
Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction
Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task.
Dynamic Compositional Graph Convolutional Network for Efficient Composite Human Motion Prediction
With potential applications in fields including intelligent surveillance and human-robot interaction, the human motion prediction task has become a hot research topic and also has achieved high success, especially using the recent Graph Convolutional Network (GCN).
Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation
Transformers have been successfully applied in the field of video-based 3D human pose estimation.
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning
The proposed method decouples functions between the decoder and the encoder by introducing a mask regressor, which predicts the masked patch representation from the visible patch representation encoded by the encoder and the decoder reconstructs the target from the predicted masked patch representation.
Ranked #3 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 10-way (20-shot)
(using extra training data)
Few-Shot 3D Point Cloud Classification
Representation Learning
+1
RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation
The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited.
Double-chain Constraints for 3D Human Pose Estimation in Images and Videos
Notably, our model achieves state-of-the-art performance on all action categories in the Human3. 6M dataset using detected 2D poses from CPN, and our code is available at: https://github. com/KHB1698/DC-GCT.
Ranked #44 on
3D Human Pose Estimation
on Human3.6M
Facial Prior Based First Order Motion Model for Micro-expression Generation
To solve this problem, this paper tries to formulate a new task called micro-expression generation and then presents a strong baseline which combines the first order motion model with facial prior knowledge.
Learning Snippet-to-Motion Progression for Skeleton-based Human Motion Prediction
Existing Graph Convolutional Networks to achieve human motion prediction largely adopt a one-step scheme, which output the prediction straight from history input, failing to exploit human motion patterns.
Integrating Human Parsing and Pose Network for Human Action Recognition
We propose an Integrating Human Parsing and Pose Network (IPP-Net) for action recognition, which is the first to leverage both skeletons and human parsing feature maps in dual-branch approach.
Ranked #7 on
Action Recognition
on NTU RGB+D 120
Joint Adversarial and Collaborative Learning for Self-Supervised Action Recognition
Considering the instance-level discriminative ability, contrastive learning methods, including MoCo and SimCLR, have been adapted from the original image representation learning task to solve the self-supervised skeleton-based action recognition task.
Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action Recognition
To address these problems, we propose an Interactive Spatiotemporal Token Attention Network (ISTA-Net), which simultaneously model spatial, temporal, and interactive relations.
Human Interaction Recognition
Skeleton Based Action Recognition
A Gated Cross-domain Collaborative Network for Underwater Object Detection
Firstly, a real-time UIE method is employed to generate enhanced images, which can improve the visibility of objects in low-contrast areas.
Explore In-Context Learning for 3D Point Cloud Understanding
With the rise of large-scale models trained on broad data, in-context learning has become a new learning paradigm that has demonstrated significant potential in natural language processing and computer vision tasks.
Cross-Modal Retrieval for Motion and Text via DopTriple Loss
On the latest HumanML3D dataset, we achieve a recall of 62. 9% for motion retrieval and 71. 5% for text retrieval (both based on R@10).
Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition
Then, channel-dependent and temporal-dependent adjacency matrices corresponding to different channels and frames are calculated to capture the spatiotemporal dependencies between skeleton joints.
Part Aware Contrastive Learning for Self-Supervised Action Recognition
This paper proposes an attention-based contrastive learning framework for skeleton representation learning, called SkeAttnCLR, which integrates local similarity and global features for skeleton-based action representations.
Graph-Guided MLP-Mixer for Skeleton-Based Human Motion Prediction
In recent years, Graph Convolutional Networks (GCNs) have been widely used in human motion prediction, but their performance remains unsatisfactory.
Ranked #3 on
Human Pose Forecasting
on Human3.6M
PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation
However, in real scenarios, the performance of PoseFormer and its follow-ups is limited by two factors: (a) The length of the input joint sequence; (b) The quality of 2D joint detection.
Ranked #8 on
3D Human Pose Estimation
on MPI-INF-3DHP
DiffMesh: A Motion-aware Diffusion-like Framework for Human Mesh Recovery from Videos
While image-based HMR methods have achieved impressive results, they often struggle to recover humans in dynamic scenarios, leading to temporal inconsistencies and non-smooth 3D motion predictions due to the absence of human motion.
Ranked #56 on
3D Human Pose Estimation
on 3DPW
Feature Completion Transformer for Occluded Person Re-identification
In this paper, different from most previous works that discard the occluded region, we propose a Feature Completion Transformer (FCFormer) to implicitly complement the semantic information of occluded parts in the feature space.
Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models
Diffusion-based generative models have recently emerged as powerful solutions for high-quality synthesis in multiple domains.
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition
In this paper, to make better use of the movement patterns introduced by extreme augmentations, a Contrastive Learning framework utilizing Abundant Information Mining for self-supervised action Representation (AimCLR) is proposed.
Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation
The modified VTE is termed as Strided Transformer Encoder (STE), which is built upon the outputs of VTE.
Ranked #2 on
3D Human Pose Estimation
on HumanEva-I
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method
To the best of our knowledge, this research represents the first comprehensive discussion of deep learning-based action recognition using 3D skeleton data.
RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices
Therefore, high efficiency object detectors on CPU-only devices are urgently-needed in industry.
Self-Refining Deep Symmetry Enhanced Network for Rain Removal
Rain removal aims to extract and remove rain streaks from images.
Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition
This deformable convolution can better utilize contextual joints for action and gesture recognition and is more robust to noisy joints.
Recognizing Human Actions as the Evolution of Pose Estimation Maps
Specifically, the evolution of pose estimation maps can be decomposed as an evolution of heatmaps, e. g., probabilistic maps, and an evolution of estimated 2D human poses, which denote the changes of body shape and body pose, respectively.
Ranked #1 on
Multimodal Activity Recognition
on UTD-MHAD
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions
Then, motion and shape cues are jointly used to generate robust and distinctive spatial-temporal interest points (STIPs): motion-based STIPs and shape-based STIPs.
Enhanced skeleton visualization for view invariant human action recognition
First, a sequence-based view invariant transform is developed to eliminate the effect of view variations on spatio-temporal locations of skeleton joints.
Ranked #2 on
Skeleton Based Action Recognition
on UWA3D
Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition
Extensive experiments on the SmartHome dataset and the large-scale NTU RGB-D dataset demonstrate that our method outperforms most of RNN-based methods, which verify the complementary property between spatial and temporal information and the robustness to noise.
Skeleton Based Action Recognition
Vocal Bursts Valence Prediction
