We present a generic neural network architecture that uses Composite Fields to detect and construct a spatio-temporal pose which is a single, connected graph whose nodes are the semantic keypoints (e. g., a person's body joints) in multiple frames.
Ranked #4 on Multi-Person Pose Estimation on COCO
Our neural network estimates human 3D body locations and their orientation with a measure of uncertainty.
Monocular and stereo visions are cost-effective solutions for 3D human localization in the context of self-driving cars or social robots.
In this work, we present an in-depth analysis of existing deep learning-based methods for modelling social interactions.
Ranked #3 on Trajectory Prediction on TrajNet++
We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots.
Ranked #9 on Keypoint Detection on COCO test-dev
We propose to (i) rethink pairwise interactions with a self-attention mechanism, and (ii) jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework.