2021/11/27 by Xueting Li, Shalini De Mello, Li, Xueting +10 · 5 citations
Computer Science · Engineering · Mathematics · #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #Field (mathematics) #Function (biology) #Generative grammar #Generative model #Human Motion and Animation #Human Pose and Action Recognition #Mathematics #Position (finance) #Representation (politics) #Trajectory #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.2111.13997
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/11/27 · openalex publication_date 2021/11/27 · arxiv updated 2021/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We propose a novel scene representation that encodes reaching distance -- the distance between any position in the scene to a goal along a feasible trajectory. We demonstrate that this environment field representation can directly guide the dynamic behaviors of agents in 2D mazes or 3D indoor scenes. Our environment field is a continuous representation and learned via a neural implicit function using discretely sampled training data. We showcase its application for agent navigation in 2D mazes, and human trajectory prediction in 3D indoor environments. To produce physically plausible and natural trajectories for humans, we additionally learn a generative model that predicts regions where humans commonly appear, and enforce the environment field to be defined within such regions. Extensive experiments demonstrate that the proposed method can generate both feasible and plausible trajectories efficiently and accurately.