Papers Motion Forecasting
“Motion Forecasting” 태그가 달린 논문 262편 · 필터 해제
Multi-Person Human Motion Forecasting in Complex Scenes
Accurately forecasting the movement of people in complex scenes requires reasoning over the past and present state of the entire environment. In this context, effectively incorporating object information and social inter…
Motion ForecastingDESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction
Advanced automation is a key technology for enhancing the safety of ground operations amidst the increasing density of commercial air traffic. While motion forecasting is a well-studied task in autonomous driving, its ap…
Autonomous DrivingMotion ForecastingSPARC: Single-Pass Scaling for Motion Forecasting with Conformal Bayesian Last Layers
Human motion forecasters are increasingly accurate and fast, but reliable deployment requires uncertainty estimates that are structured, calibrated, and efficient. Bayesian and ensemble-based uncertainty estimates often …
Motion ForecastingTowards Surgical World-Action Modeling: A Preliminary Joint Visual-Trajectory Forecasting for Surgical Motion Planning
Reliable surgical planning requires models to anticipate not only how instruments will move, but also how the operative visual state will evolve together with such motion. Existing approaches typically treat future scene…
Trajectory ForecastingTrajectory PredictionMotion ForecastingScene GenerationMotionForesight: Re-purposing Video Models for Future 3D Scene-Flow Prediction
Humans can infer how objects are likely to move from passive observation: a cup may be lifted, a drawer may slide, and a lid may rotate shut. Such predictions expose the physical consequences of interaction needed to act…
Motion ForecastingVideo PredictionPRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment
Urban intersections are among the most hazardous locations in road networks, posing significant risks to vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists. The complexity of multi-agent interacti…
Trajectory PredictionMotion ForecastingEgo-Human Motion Prediction with 3D-Aware LLM
Anticipating human motion from an egocentric perspective is fundamental for proactive assistance in AR/VR, human-robot collaboration, and embodied AI. While recent works incorporate language as a semantic prior to reduce…
Motion ForecastingPoint TrackingIMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction
Multi-agent motion prediction is essential for automated vehicles to understand the intentions of surrounding vehicles. However, previous prediction-based and anchor-based methods have limitations in mode diversity and p…
Trajectory PredictionMotion ForecastingCRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining
Camera-radar (CR) fusion is a practical sensing configuration for autonomous driving, but existing models are typically trained with task-specific supervision, limiting reusable representation learning. We present CRISP,…
Representation LearningAutonomous DrivingMotion ForecastingPoint CloudsLearning When to Listen: Gated Affect Fusion for Human Motion Prediction
Human motion forecasting in unconstrained real-world videos remains challenging due to the ambiguity of future behaviors and the presence of noisy multimodal observations. While facial affect potentially provides complem…
Motion ForecastingLAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction
Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizing standard displacement errors, they of…
Trajectory PredictionAutonomous DrivingMotion ForecastingMolmoMotion: Forecasting Point Trajectories in 3D with Language Instruction
Motion forecasting is central to visual intelligence: agents must anticipate how objects will move in order to plan actions, reason about physical interactions, and synthesize realistic futures. We argue that 3D points i…
Robot ManipulationMotion ForecastingFuture Dynamic 3D Reconstruction: Toward 3D World Modeling with Disentangled Ego-Motion
Forecasting the evolution of dynamic environments is crucial for autonomous agents. While generative world models have achieved high photorealism in 2D video synthesis by mixing ego-motion and environmental dynamics with…
Zero-shot GeneralizationMotion Forecasting3D ReconstructionStandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets
Autonomous driving has shifted from modular perception-prediction-planning stacks toward end-to-end (E2E) models that map sensor inputs directly to vehicle control, often regularized by auxiliary tasks such as 3D detecti…
Autonomous DrivingMotion ForecastingMode-as-Sequence: Translating Multimodal Motion Prediction into Unified Sequential Mode Modeling
Multimodal motion forecasting is inherently under-supervised: each training scene provides only one realized future, yet multiple plausible futures exist. This sparse supervision often leads to mode collapse (redundant h…
Motion ForecastingVL-DPO: Vision-Language-Guided Finetuning for Preference-Aligned Autonomous Driving
The rapid growth of autonomous driving datasets has enabled the scaling of powerful motion forecasting models. While large-scale pretraining provides strong performance, the standard imitation objective may not fully cap…
Autonomous DrivingMotion Forecasting4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving
We present 4DLidarOpen, a large-scale open multi-modal dataset for autonomous driving, centered on 4D frequency-modulated continuous-wave (FMCW) Lidar sensing. Unlike conventional time-of-flight Lidar datasets that mainl…
Scene Understanding3D Object DetectionMotion ForecastingAutonomous DrivingDynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation
Traditional Simultaneous Localization and Mapping (SLAM) algorithms rely heavily on the static environment assumption, which severely limits their applicability in real-world spaces populated by moving entities, such as …
Motion ForecastingRobot NavigationRecall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank
Motion forecasting often requires trading interpretability for predictive accuracy. Standard anchor-based architectures rely on opaque latent queries that are highly prone to latent collapse, or naive trajectory sampling…
Contrastive LearningMotion ForecastingOmniRobotHome: A Multi-Camera Home Platform for Real-Time Human-Robot Interaction
Robots in homes must continuously sense the people around them, yet most prior work relies on limited or offline perception. We argue that perception quality is the dominant factor governing what interaction is achievabl…
Motion Forecasting