Motion Model

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4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

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Latest papers 25

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Generative Interactions: Weaving Multiparty Human Motion with Bilevel Latent Dynamics
Sep 29, 2026Ojas Shirekar, Yash Surange, Agustinas Jučas +1Interaction Prediction Human Motion
TU Delft Delft, The Netherlands · University of Oxford Oxford, UK
OTRetarget: Joint Robot and Object Motion Retargeting via Optimal Transport
Sep 29, 2026Guillaume Besset, Erwann Carn, Timothée Carecchio +5Cross-Embodiment Retargeting Motion Model
Inria, Département d’Informatique de l’École Normale Supérieure, PSL Research University, Paris, France
CDKF-Track: Cluster-aware Data-Driven Kalman Filtering for Cooperative 3D Multi-Object Tracking
Sep 22, 2026Maria Damanaki, Nikos Piperigkos, Alexandros Gkillas +1Multi-Object Tracking 3D Object Detection
Industrial Systems Institute, Athena Research Center, Patras Science Park, Greece · 3Dpt. of Informatics & Telecom., University of Ioannina, Arta, Greece · Avisense.AI, Patras, Greece +1
CompAdapt: Adaptable Composite Motion Modeling for Physics-Consistent Text-to-Video Generation
Sep 18, 2026Haoran Qin, Renlong Wu, Tianyu Huang +3Text-To-Video Generation Model Text-To-Video Diffusion Models
Harbin Institute of Technology, China · Taobao, Alibaba Group, China
FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations
Sep 17, 2026Kevin Qu, Tao Sun, Massimiliano Viola +53D Models Motion Model
Stanford University · ETH Zürich
PhysStream: Streaming Physics-Grounded Video Generation with Structured Scene Memory and Fine-Grained Motion Control
Sep 15, 2026Chuhao Chen, Peter Wonka, Chaoyang Wang +4Video Generation Indoor Scene Generation
University of Pennsylvania, Philadelphia, USA · Snap Inc., Santa Monica, USA · KAUST, Thuwal, Saudi Arabia
GenTrack3: Hybrid Stochastic-Deterministic Online Multi-Object Tracking with Cluster-Aware Association
Aug 10, 2026Toan Van Nguyen, Rasmus G. K. Christiansen, Dirk Kraft +1Multi-Object Tracking Motion Model
*SDU Robotics, University of Southern Denmark
Residual Flow Matching with Dynamic Cross-Interaction for 3D Multi-Person Motion Prediction
Aug 4, 2026Wei Wei, Yinyuan Zhao, Ruixuan YuHuman Motion Prediction Contrastive Flow Matching
Shandong University
FAST-GS: Frequency Aware Space-time Gaussian Splatting for Photorealistic Dynamic Novel View Synthesis
Aug 3, 2026Zhengyang Zhang, Ziyu Lu, PengCheng Li +64D Reconstruction Motion Model
1Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Central Media Technology Institute, Huawei
DAP-Pose: Deep Temporal Alignment and Physics-aware Cross-modal Sensor Fusion for Robust Pose Estimation
Jul 26, 2026Jianhan Lin, Yuchu Qin, Jiateng Yuan +2Category-Level Object Pose Estimation Multi-Sensor Fusion
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China · International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China · University of Chinese Academy of Sciences, School of Electronic, Electrical and Communication Engineering, Beijing 100049, China +1
OmniX: Any-view and Any-time 4D Reconstruction via Feed-forward Trajectory Fields
Jul 12, 2026Yanqin Jiang, Tengfei Wang, Zhengwei Wang +64D Reconstruction Motion Model
MAIS, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · Tencent Hunyuan +3
Grassmannian Splatting I: Moving rank-2 Spacetime Surfels for Dynamic Scene Rendering
Jul 11, 2026Aaron Maurice Berman, Shantanu DaveMotion Model
SAMoR: Motion Modelling for Articulated Objects of Any Skeleton and Topology
Jul 2, 2026Yuhao Zhang, Gerard Pons-Moll, Tolga BirdalSkeleton Humanml3D
Imperial College London · University of Tübingen, Tübingen AI Center
FrameONE: Hierarchical Motion Modeling for Universal Multi-View Echocardiographic Keyframe Detection
Jul 1, 2026Rusi Chen, Yuhao Huang, Hongyuan Zhang +4Echocardiography Cardiac Motion
Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China · Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hongkong, China · School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China +2
CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking
Jun 29, 2026Buyin Deng, Kai Luo, Lingxin Huang +5Multi-Object Tracking Motion Model
School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China · School of Advanced Technology, Xi’an Jiaotong-Liverpool University, China · Suzhou VSDeep Intelligent Technology Co., Ltd., China +1
Social Structure Matters in 3D Human-Human Interaction Generation
Jun 23, 2026Zhongju Wang, Beier Wang, Yatao Bian +6Human Motion Generation Articulated Objects
University of New South Wales · National University of Singapore · NVIDIA +3
Hardness of Multi-Agent Path Finding on Trees: A Unified Approach
Jun 4, 2026Tzvika GeftMulti-Agent Path Finding Np-Hard
Rutgers University, NJ, USA · NJ, USA
AI Level of Detail: Distance-Aware ML Model Precision Selection for Real-Time Human Motion Prediction in Games
Jun 4, 2026Mathew VargheseHuman Motion Prediction Motion Model
University of Washington Seattle, WA, USA
MPMWorlds: Material-Point-Method Simulations for Inferring and Extrapolating Physical Dynamics
Jun 1, 2026Žiga Kovačič, Kevin EllisPhysics Simulation Motion Model
Cornell University
Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction
May 29, 2026Mungyeom Kim, Minkyeong Jeon, Honggyu An +104D Reconstruction Feed-Forward 3D Gaussian
KAIST AI · ETH Zürich, ETH AI Center · Sony AI +1
Learning a Particle Dynamics Model with Real-world Videos
May 22, 2026Chanho Kim, Suhas V. Sumukh, Li FuxinVideo World Models Particle Dynamics
Oregon State University
EchoTracker2: Enhancing Myocardial Point Tracking by Modeling Local Motion
May 12, 2026Md Abulkalam Azad, Vegard Holmstrøm, John Nyberg +4Cardiac Motion Motion Model
Norwegian University of Science and Technology, Trondheim, Norway · Clinic of Cardiology, St. Olavs Hospital, Trondheim, Norway · SINTEF Digital, Trondheim, Norway
Time-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking
May 1, 2026Nhat-Tan Do, Le-Huy Tu, Nhi Ngoc-Yen Nguyen +2Multi-Object Tracking Motion Model
Faculty of Information Science and Engineering, University of Information Technology, Ho Chi Minh City, Vietnam. · Vietnam National University, Ho Chi Minh City, Vietnam.
Rheos: Modelling Continuous Motion Dynamics in Hierarchical 3D Scene Graphs
Mar 9, 2026Iacopo Catalano, Francesco Verdoja, Javier Civera +23D Scene Graphs Motion Model
University of Turku, Finland · School of Electrical Engineering, Aalto University, Finland · University of Zaragoza, Spain +2
Towards Accurate State Estimation: Motion Dynamics Kalman Filter for 3D Multi-Object Tracking
May 12, 2025Mohamed Nagy, Naoufel Werghi, Bilal Hassan +2Multi-Object Tracking Extended Kalman Filter
Khalifa University, Abu Dhabi, UAE · New York University of Abu Dhabi

Sep 29, 2026, cs.AIJ/K move · Enter open · S save

Generative Interactions: Weaving Multiparty Human Motion with Bilevel Latent Dynamics

Ojas Shirekar, Yash Surange, Agustinas Jučas, Chirag Raman

TU Delft Delft, The Netherlands · University of Oxford Oxford, UK

Interaction Prediction Human Motion Motion Model Intermediate Latent States Latent Dynamics Global Coordination

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Human social behaviour is not a collection of independent motions, but a jointly organised process in which group dynamics and individual variation continuously shape one another. Yet existing social motion models often prioritise plausible trajectories while leaving interaction state implicit, limiting their ability to transfer across groups, tasks, and partial-observation regimes. To address this gap, we introduce Bilevel Representations for Agent Interaction Dynamics (BRAID), a hierarchical sequential latent-variable model for generative multi-person interaction. BRAID explicitly formulates social motion generation as a meta-transfer learning problem: shared interaction priors are learned across datasets and adapted through arbitrary context sets of observed people and joints. The model represents each scene through a group-level latent state that captures shared interaction dynamics and person-level latent states that capture individual behaviour conditioned on the evolving group context. This modelling choice enables coherent generation under full, sparse, or partial observations while exposing compact social-state vectors that can serve as an interface for downstream embodied-agent systems. We evaluate BRAID under a unified SMPL-based representation on social forecasting, tracking and in-filling, and response generation, using metrics that assess not only reconstruction accuracy but also realism, diversity, temporal alignment, and interpersonal coordination. We further analyse the hierarchical latent space, showing that it captures separable group- and individual-level structure.

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