Human Pose and Action Recognition

Derniers papiers

• Student Classroom Behavior Recognition Based on Improved YOLOv8s

  Xiang Gao, Shuai Hang · 1 mai 2026

  In classroom teaching, student behavior can reflect their learning state and classroom participation, which is of great significance for teaching quality analysis. To address the problems of dense student targets, numerous small objects, frequent occlusions, and imbalanced class distribution in real…

• TransVLM: A Vision-Language Framework and Benchmark for Detecting Any Shot Transitions

  Ce Chen, Yi Ren, Yuanming Li, Viktor Goriachko, Zhenhui Ye, Zujin Guo, Zhibin Hong, Mingming Gong · 1 mai 2026

  Traditional Shot Boundary Detection (SBD) inherently struggles with complex transitions by formulating the task around isolated cut points, frequently yielding corrupted video shots. We address this fundamental limitation by formalizing the Shot Transition Detection (STD) task. Rather than searching…

• Featurising Pixels from Dynamic 3D Scenes with Linear In-Context Learners

  Nikita Araslanov, Martin Sundermeyer, Hidenobu Matsuki, David Joseph Tan, Federico Tombari · 30 avril 2026

  One of the most exciting applications of vision models involve pixel-level reasoning. Despite the abundance of vision foundation models, we still lack representations that effectively embed spatio-temporal properties of visual scenes at the pixel level. Existing frameworks either train on image-base…

• Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising

  Jun Guo, Qiwei Li, Peiyan Li, Zilong Chen, Nan Sun, Yifei Su, Heyun Wang, Yuan Zhang, Xinghang Li, Huaping Liu · 30 avril 2026

  We propose X-WAM, a Unified 4D World Model that unifies real-time robotic action execution and high-fidelity 4D world synthesis (video + 3D reconstruction) in a single framework, addressing the critical limitations of prior unified world models (e.g., UWM) that only model 2D pixel-space and fail to …

• Emotion-Conditioned Short-Horizon Human Pose Forecasting with a Lightweight Predictive World Model

  Jingni Huang, Peter Bloodsworth · 29 avril 2026

  Short-term human pose prediction plays a crucial role in interactive systems, assistive robots, and emotion-aware human-computer interaction[1-3]. While current trajectory prediction models primarily rely on geometric motion cues, they often overlook the underlying emotional signals influencing huma…

• From Skeletons to Pixels: Few-Shot Precise Event Spotting via Representation and Prediction Distillation

  Zhong Han Ervin Yeoh, Jiang Kan · 29 avril 2026

  Precise Event Spotting (PES) is essential in fast-paced sports such as tennis, where fine-grained events occur within very short temporal windows. Accurate frame-level localization is challenging because of motion blur, subtle action differences, and limited annotated data. We study two complementar…

• Unconstrained Multi-view Human Pose Estimation with Algebraic Priors

  Xiaolin Qin, Qianlei Wang, Jiacen Liu, Chaoning Zhang, Fei Zhu, Zhang Yi · 29 avril 2026

  Recovering 3D human pose from multi-view imagery typically relies on precise camera calibration, which is often unavailable in real-world scenarios, thereby severely limiting the applicability of existing methods. To overcome this challenge, we propose an unconstrained framework that synergizes deep…

• 2D Pre-Training for 3D Pose Estimation

  Liyao Jiang, Ruichen Chen, Keith G. Mills · 28 avril 2026

  Pre-training is a general method that is used in a range of deep learning tasks. By first training a model on one task, and then further training on the downstream task used for final evaluation, the model is forced to learn a more general understanding of the input data. While pre-training has been…

• EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms

  Brian VanVoorst, Nicholas Walczak, Christopher Gilleo, Charles Meissner, Fabio Felix, Iran Roman, Bea Steers, Claudio Silva, Yuhan Shen, Zijia Lu, Shih-Po Lee, Ehsan Elhamifar · 27 avril 2026

  This paper introduces EgoMAGIC (Medical Assistance, Guidance, Instruction, and Correction), an egocentric medical activity dataset collected as part of DARPA's Perceptually-enabled Task Guidance (PTG) program. This dataset comprises 3,355 videos of 50 medical tasks, with at least 50 labeled videos p…

• LEXIS: LatEnt ProXimal Interaction Signatures for 3D HOI from an Image

  Dimitrije Anti\'c, Alvaro Budria, George Paschalidis, Sai Kumar Dwivedi, Dimitrios Tzionas · 23 avril 2026

  Reconstructing 3D Human-Object Interaction from an RGB image is essential for perceptive systems. Yet, this remains challenging as it requires capturing the subtle physical coupling between the body and objects. While current methods rely on sparse, binary contact cues, these fail to model the conti…

• Explicit Dropout: Deterministic Regularization for Transformer Architectures

  Vidhi Agrawal, Illia Oleksiienko, Alexandros Iosifidis · 23 avril 2026

  Dropout is a widely used regularization technique in deep learning, but its effects are typically realized through stochastic masking rather than explicit optimization objectives. We propose a deterministic formulation that expresses dropout as an additive regularizer directly incorporated into the …

• UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling

  Boyu Chen, Yi Chen, Lu Qiu, Jerry Bai, Yuying Ge, Yixiao Ge · 22 avril 2026

  Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data. While massive egocentric human data offers a scalable alternative, bridging the cross-embodiment chasm remains a fundamental challenge due to kinematic mismatches. We introduce UniT (Unified Latent Action Tokenizer v…

• UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation

  Haopeng Chen, Yihao Ai, Kabeen Kim, Robby T. Tan, Yixin Chen, Bo Wang · 14 avril 2026

  Low-visibility scenarios, such as low-light conditions, pose significant challenges to human pose estimation due to the scarcity of annotated low-light datasets and the loss of visual information under poor illumination. Recent domain adaptation techniques attempt to utilize well-lit labels by augme…

• Attention-Guided Dual-Stream Learning for Group Engagement Recognition: Fusing Transformer-Encoded Motion Dynamics with Scene Context via Adaptive Gating

  Saniah Kayenat Chowdhury, Muhammad E. H. Chowdhury · 14 avril 2026

  Student engagement is crucial for improving learning outcomes in group activities. Highly engaged students perform better both individually and contribute to overall group success. However, most existing automated engagement recognition methods are designed for online classrooms or estimate engageme…

• Inferring Dynamic Physical Properties from Video Foundation Models

  Guanqi Zhan, Xianzheng Ma, Weidi Xie, Andrew Zisserman · 14 avril 2026

  We study the task of predicting dynamic physical properties from videos. More specifically, we consider physical properties that require temporal information to be inferred: elasticity of a bouncing object, viscosity of a flowing liquid, and dynamic friction of an object sliding on a surface. To thi…

• AI Driven Soccer Analysis Using Computer Vision

  Adrian Manchado, Tanner Cellio, Jonathan Keane, Yiyang Wang · 13 avril 2026

  Sport analysis is crucial for team performance since it provides actionable data that can inform coaching decisions, improve player performance, and enhance team strategies. To analyze more complex features from game footage, a computer vision model can be used to identify and track key entities fro…

• Frequency-Enhanced Diffusion Models: Curriculum-Guided Semantic Alignment for Zero-Shot Skeleton Action Recognition

  Yuxi Zhou, Zhengbo Zhang, Jingyu Pan, Zhiyu Lin, Zhigang Tu · 13 avril 2026

  Human action recognition is pivotal in computer vision, with applications ranging from surveillance to human-robot interaction. Despite the effectiveness of supervised skeleton-based methods, their reliance on exhaustive annotation limits generalization to novel actions. Zero-Shot Skeleton Action Re…

• AugLift: Depth-Aware Input Reparameterization Improves Domain Generalization in 2D-to-3D Pose Lifting

  Nikolai Warner, Wenjin Zhang, Hamid Badiozamani, Irfan Essa, Apaar Sadhwani · 9 avril 2026

  Lifting-based 3D human pose estimation infers 3D joints from 2D keypoints but generalizes poorly because $(x,y)$ coordinates alone are an ill-posed, sparse representation that discards geometric information modern foundation models can recover. We propose \emph{AugLift}, which changes the representa…

• Why Can't I Open My Drawer? Mitigating Object-Driven Shortcuts in Zero-Shot Compositional Action Recognition

  Geo Ahn, Inwoong Lee, Taeoh Kim, Minho Shim, Dongyoon Wee, Jinwoo Choi · 9 avril 2026

  Zero-Shot Compositional Action Recognition (ZS-CAR) requires recognizing novel verb-object combinations composed of previously observed primitives. In this work, we tackle a key failure mode: models predict verbs via object-driven shortcuts (i.e., relying on the labeled object class) rather than tem…

• MoRight: Motion Control Done Right

  Shaowei Liu, Xuanchi Ren, Tianchang Shen, Huan Ling, Saurabh Gupta, Shenlong Wang, Sanja Fidler, Jun Gao · 9 avril 2026

  Generating motion-controlled videos--where user-specified actions drive physically plausible scene dynamics under freely chosen viewpoints--demands two capabilities: (1) disentangled motion control, allowing users to separately control the object motion and adjust camera viewpoint; and (2) motion ca…

• Human Interaction-Aware 3D Reconstruction from a Single Image

  Gwanghyun Kim, Junghun James Kim, Suh Yoon Jeon, Jason Park, Se Young Chun · 8 avril 2026

  Reconstructing textured 3D human models from a single image is fundamental for AR/VR and digital human applications. However, existing methods mostly focus on single individuals and thus fail in multi-human scenes, where naive composition of individual reconstructions often leads to artifacts such a…

• Why Can't I Open My Drawer? Mitigating Object-Driven Shortcuts in Zero-Shot Compositional Action Recognition

  Geo Ahn, Inwoong Lee, Taeoh Kim, Minho Shim, Dongyoon Wee, Jinwoo Choi · 8 avril 2026

  Zero-Shot Compositional Action Recognition (ZS-CAR) requires recognizing novel verb-object combinations composed of previously observed primitives. In this work, we tackle a key failure mode: models predict verbs via object-driven shortcuts (i.e., relying on the labeled object class) rather than tem…

• MoViD: View-Invariant 3D Human Pose Estimation via Motion-View Disentanglement

  Yejia Liu, Hengle Jiang, Haoxian Liu, Runxi Huang, Xiaomin Ouyang · 7 avril 2026

  3D human pose estimation is a key enabling technology for applications such as healthcare monitoring, human-robot collaboration, and immersive gaming, but real-world deployment remains challenged by viewpoint variations. Existing methods struggle to generalize to unseen camera viewpoints, require la…

• CoLoRSMamba: Conditional LoRA-Steered Mamba for Supervised Multimodal Violence Detection

  Damith Chamalke Senadeera, Dimitrios Kollias, Gregory Slabaugh · 7 avril 2026

  Violence detection benefits from audio, but real-world soundscapes can be noisy or weakly related to the visible scene. We present CoLoRSMamba, a directional Video to Audio multimodal architecture that couples VideoMamba and AudioMamba through CLS-guided conditional LoRA. At each layer, the VideoMam…

• Learning Additively Compositional Latent Actions for Embodied AI

  Hangxing Wei, Xiaoyu Chen, Chuheng Zhang, Tim Pearce, Jianyu Chen, Alex Lamb, Li Zhao, Jiang Bian · 7 avril 2026

  Latent action learning infers pseudo-action labels from visual transitions, providing an approach to leverage internet-scale video for embodied AI. However, most methods learn latent actions without structural priors that encode the additive, compositional structure of physical motion. As a result, …