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Found 10 papers, 2 papers with code
DHRNet: A Dual-Path Hierarchical Relation Network for Multi-Person Pose Estimation
Multi-person pose estimation (MPPE) presents a formidable yet crucial challenge in computer vision.
Towards more realistic human motion prediction with attention to motion coordination
However, the motion coordination, a global joint relation reflecting the simultaneous cooperation of all joints, is usually weakened because it is learned from part to whole progressively and asynchronously.
Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference
In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success.
GeRM: A Generalist Robotic Model with Mixture-of-experts for Quadruped Robot
Multi-task robot learning holds significant importance in tackling diverse and complex scenarios.
QUAR-VLA: Vision-Language-Action Model for Quadruped Robots
Within this framework, a notable challenge lies in aligning fine-grained instructions with visual perception information.
Expressive Forecasting of 3D Whole-body Human Motions
Human motion forecasting, with the goal of estimating future human behavior over a period of time, is a fundamental task in many real-world applications.
Instance-incremental Scene Graph Generation from Real-world Point Clouds via Normalizing Flows
This work introduces a new task of instance-incremental scene graph generation: Given a scene of the point cloud, representing it as a graph and automatically increasing novel instances.
Uncertainty-aware Human Motion Prediction
It is far more enough for current approaches in actual scenarios because people can't know how to interact with the machine without the evaluation of prediction, and unreliable predictions may mislead the machine to harm the human.
An Attractor-Guided Neural Networks for Skeleton-Based Human Motion Prediction
However, the global coordination of all joints, which reflects human motion's balance property, is usually weakened because it is learned from part to whole progressively and asynchronously.
TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction
And the global temporal co-occurrence features represent the co-occurrence relationship that different subsequences in a complex motion sequence are appeared simultaneously, which can be obtained automatically with our proposed TrajectoryNet by reorganizing the temporal information as the depth dimension of the input tensor.
