sgan
A PyTorch implementation of Social GAN for predicting socially acceptable human trajectories using generative adversarial networks.
What is sgan?
Social GAN is a PyTorch-based research implementation for predicting socially acceptable human trajectories in crowded environments. It uses a generative adversarial network (GAN) combined with a recurrent sequence-to-sequence model to forecast future paths while considering interpersonal interactions. The model addresses the problem of multimodal and socially compliant motion prediction in complex scenarios like pedestrian navigation.
Researchers and practitioners in computer vision, robotics, and human-computer interaction who work on trajectory forecasting, social navigation, or human behavior modeling. It is particularly relevant for those developing autonomous systems that need to understand and predict human motion.
Developers choose Social GAN for its novel social pooling mechanism that aggregates information across people, enabling more accurate and socially compliant predictions. It provides an open-source, reproducible implementation of a state-of-the-art CVPR paper with pretrained models for multiple datasets.
Overview
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
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