  There are five types of trajectory prediction tasks: deterministic,
stochastic, domain adaptation, momentary observation, and few-shot. These
associated tasks are defined by various factors, such as the length of input
paths, data split and pre-processing methods. Interestingly, even though they
commonly take sequential coordinates of observations as input and infer future
paths in the same coordinates as output, designing specialized architectures
for each task is still necessary. For the other task, generality issues can
lead to sub-optimal performances. In this paper, we propose SingularTrajectory,
a diffusion-based universal trajectory prediction framework to reduce the
performance gap across the five tasks. The core of SingularTrajectory is to
unify a variety of human dynamics representations on the associated tasks. To
do this, we first build a Singular space to project all types of motion
patterns from each task into one embedding space. We next propose an adaptive
anchor working in the Singular space. Unlike traditional fixed anchor methods
that sometimes yield unacceptable paths, our adaptive anchor enables correct
anchors, which are put into a wrong location, based on a traversability map.
Finally, we adopt a diffusion-based predictor to further enhance the prototype
paths using a cascaded denoising process. Our unified framework ensures the
generality across various benchmark settings such as input modality, and
trajectory lengths. Extensive experiments on five public benchmarks demonstrate
that SingularTrajectory substantially outperforms existing models, highlighting
its effectiveness in estimating general dynamics of human movements. Code is
publicly available at https://github.com/inhwanbae/SingularTrajectory .
