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motion prediction

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Showing 51–100 of 597 papers

TitleStatusHype
ALIEN: Implicit Neural Representations for Human Motion Prediction under Arbitrary Latency—0
LAL: Enhancing 3D Human Motion Prediction with Latency-aware Auxiliary Learning—0
Temporal Dynamics Decoupling with Inverse Processing for Enhancing Human Motion Prediction—0
Spatio-Temporal Multi-Subgraph GCN for 3D Human Motion Prediction—0
DeepMF: Deep Motion Factorization for Closed-Loop Safety-Critical Driving Scenario Simulation—0
Joint Perception and Prediction for Autonomous Driving: A SurveyCode2
Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration—0
GPD-1: Generative Pre-training for DrivingCode2
Robust Multiple Description Neural Video Codec with Masked Transformer for Dynamic and Noisy Networks—0
PriorMotion: Generative Class-Agnostic Motion Prediction with Raster-Vector Motion Field Priors—0
Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-ConceptCode0
PROFIT: A Specialized Optimizer for Deep Fine Tuning—0
TAROT: Targeted Data Selection via Optimal TransportCode1
DECODE: Domain-aware Continual Domain Expansion for Motion PredictionCode0
MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning—0
Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals—0
DriveMLLM: A Benchmark for Spatial Understanding with Multimodal Large Language Models in Autonomous DrivingCode2
ModeSeq: Taming Sparse Multimodal Motion Prediction with Sequential Mode Modeling—0
DiVR: incorporating context from diverse VR scenes for human trajectory prediction—0
UnityGraph: Unified Learning of Spatio-temporal features for Multi-person Motion Prediction—0
Relation Learning and Aggregate-attention for Multi-person Motion Prediction—0
Multi-Transmotion: Pre-trained Model for Human Motion PredictionCode1
Multi-modal Motion Prediction using Temporal Ensembling with Learning-based Aggregation—0
Pedestrian motion prediction evaluation for urban autonomous drivingCode0
LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature IntegrationCode0
LoRD: Adapting Differentiable Driving Policies to Distribution ShiftsCode0
ESVO2: Direct Visual-Inertial Odometry with Stereo Event CamerasCode2
SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion PredictionCode1
MDMP: Multi-modal Diffusion for supervised Motion Predictions with uncertaintyCode1
Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction—0
BadHMP: Backdoor Attack against Human Motion Prediction—0
Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation—0
S2O: Static to Openable Enhancement for Articulated 3D Objects—0
CASPFormer: Trajectory Prediction from BEV Images with Deformable Attention—0
Data-efficient Trajectory Prediction via Coreset Selection—0
Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference SpeedCode1
Bayesian-Optimized One-Step Diffusion Model with Knowledge Distillation for Real-Time 3D Human Motion Prediction—0
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning—0
Annealed Winner-Takes-All for Motion ForecastingCode1
ScaleFlow++: Robust and Accurate Estimation of 3D Motion from VideoCode1
DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-DrivingCode1
Robust Dual Gaussian Splatting for Immersive Human-centric Volumetric Videos—0
Synergy and Synchrony in Couple Dances—0
Multi-scale Temporal Fusion Transformer for Incomplete Vehicle Trajectory Prediction—0
MambaTrack: A Simple Baseline for Multiple Object Tracking with State Space Model—0
Towards Practical Human Motion Prediction with LiDAR Point Clouds—0
Pedestrian Motion Prediction Using Transformer-based Behavior Clustering and Data-Driven Reachability Analysis—0
Past Movements-Guided Motion Representation Learning for Human Motion PredictionCode0
Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting ModelsCode1
Flow-guided Motion Prediction with Semantics and Dynamic Occupancy Grid Maps—0
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