This paper tackles the problem of human motion prediction, consisting in forecasting future
body poses from historically observed sequences. State-of-the-art approaches provide good …

We have recently seen tremendous progress in diffusion advances for generating realistic
human motions. Yet, they largely disregard the multi-human interactions. In this paper, we …

We propose Hi4D, a method and dataset for the auto analysis of physically close human-
human interaction under prolonged contact. Robustly disentangling several in-contact …

Abstract We propose Social Diffusion, a novel method for short-term and long-term
forecasting of the motion of multiple persons as well as their social interactions. Jointly …

The analysis of the ubiquitous human-human interactions is pivotal for understanding
humans as social beings. Existing human-human interaction datasets typically suffer from …

Multi-person pose forecasting remains a challenging problem, especially in modeling fine-
grained human body interaction in complex crowd scenarios. Existing methods typically …

Multi-person motion prediction is a challenging problem due to the dependency of motion on
both individual past movements and interactions with other people. Transformer-based …

This paper aims to deal with the ignored real-world complexities in prior work on human
motion forecasting, emphasizing the social properties of multi-person motion, the diversity of …

Unsupervised self-rehabilitation exercises and physical training can cause serious injuries if
performed incorrectly. We introduce a learning-based framework that identifies the mistakes …

The task of collaborative human pose forecasting stands for predicting the future poses of
multiple interacting people, given those in previous frames. Predicting two people in …