The Garden of Forking Paths:
Towards Multi-Future Trajectory Prediction
Junwei Liang1, Lu Jiang2, Kevin Murphy2, Ting Yu3, Alexander Hauptmann1
1Carnegie Mellon University, 2Google Research, 3Google Cloud AI
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
This paper studies the problem of predicting the distribution over multiple possible future paths of people as they move through various visual scenes. We make two main contributions. The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals. This provides the first benchmark for quantitative evaluation of the models to predict multi-future trajectories.
The second contribution is a new model to generate multiple plausible future trajectories, which contains novel designs of using multi-scale location encodings and convolutional RNNs over graphs. We refer to our model as Multiverse. We show that our model achieves the best results on our dataset, as well as on the real-world VIRAT/ActEV dataset (which just contains one possible future).
High Definition Video Demonstration

The Forking Paths Dataset provides high resolution videos along with accurate bounding box and scene semantic segmentation annotations.

Demo Video

Scene Reconstruction from Real Videos

We recreate the static scene and dynamic trajectories from real-world videos.

Editing Interface

This is the editing interface. We can easily add, delete and edit agent behaviors in each scenario. We also provide pan-tilt-zoom operations so that we could easily find the desired camera view for recording videos.

Annotation Procedure

This is the human annotation procedure. Annotators are provided with a bird-eye view of the scene first and then they are asked to control the agent to go to the destination.

Human Annotations of Multi-future Trajectories
Release Log