  Human behavior has the nature of indeterminacy, which requires the pedestrian
trajectory prediction system to model the multi-modality of future motion
states. Unlike existing stochastic trajectory prediction methods which usually
use a latent variable to represent multi-modality, we explicitly simulate the
process of human motion variation from indeterminate to determinate. In this
paper, we present a new framework to formulate the trajectory prediction task
as a reverse process of motion indeterminacy diffusion (MID), in which we
progressively discard indeterminacy from all the walkable areas until reaching
the desired trajectory. This process is learned with a parameterized Markov
chain conditioned by the observed trajectories. We can adjust the length of the
chain to control the degree of indeterminacy and balance the diversity and
determinacy of the predictions. Specifically, we encode the history behavior
information and the social interactions as a state embedding and devise a
Transformer-based diffusion model to capture the temporal dependencies of
trajectories. Extensive experiments on the human trajectory prediction
benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the
superiority of our method. Code is available at
https://github.com/gutianpei/MID.
