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Auxiliary tasks benefit 3d skeleton-based human motion prediction
Exploring spatial-temporal dependencies from observed motions is one of the core
challenges of human motion prediction. Previous methods mainly focus on dedicated …
challenges of human motion prediction. Previous methods mainly focus on dedicated …
Singulartrajectory: Universal trajectory predictor using diffusion model
There are five types of trajectory prediction tasks: deterministic stochastic domain adaptation
momentary observation and few-shot. These associated tasks are defined by various factors …
momentary observation and few-shot. These associated tasks are defined by various factors …
Can language beat numerical regression? language-based multimodal trajectory prediction
Abstract Language models have demonstrated impressive ability in context understanding
and generative performance. Inspired by the recent success of language foundation models …
and generative performance. Inspired by the recent success of language foundation models …
[HTML][HTML] Gatraj: A graph-and attention-based multi-agent trajectory prediction model
Trajectory prediction has been a long-standing problem in intelligent systems like
autonomous driving and robot navigation. Models trained on large-scale benchmarks have …
autonomous driving and robot navigation. Models trained on large-scale benchmarks have …
T4p: Test-time training of trajectory prediction via masked autoencoder and actor-specific token memory
Trajectory prediction is a challenging problem that requires considering interactions among
multiple actors and the surrounding environment. While data-driven approaches have been …
multiple actors and the surrounding environment. While data-driven approaches have been …
Equivariant spatio-temporal attentive graph networks to simulate physical dynamics
Learning to represent and simulate the dynamics of physical systems is a crucial yet
challenging task. Existing equivariant Graph Neural Network (GNN) based methods have …
challenging task. Existing equivariant Graph Neural Network (GNN) based methods have …
Lanecpp: Continuous 3d lane detection using physical priors
Monocular 3D lane detection has become a fundamental problem in the context of
autonomous driving which comprises the tasks of finding the road surface and locating lane …
autonomous driving which comprises the tasks of finding the road surface and locating lane …
A survey of geometric graph neural networks: Data structures, models and applications
Geometric graph is a special kind of graph with geometric features, which is vital to model
many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical …
many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical …
Skeleton-in-context: Unified skeleton sequence modeling with in-context learning
In-context learning provides a new perspective for multi-task modeling for vision and NLP.
Under this setting the model can perceive tasks from prompts and accomplish them without …
Under this setting the model can perceive tasks from prompts and accomplish them without …
Socialcircle: Learning the angle-based social interaction representation for pedestrian trajectory prediction
Analyzing and forecasting trajectories of agents like pedestrians and cars in complex scenes
has become more and more significant in many intelligent systems and applications. The …
has become more and more significant in many intelligent systems and applications. The …