SPOTR: Spatio-temporal Pose Transformers for Human Motion Prediction

TL;DR

This paper introduces SPOTR, a non-autoregressive Transformer model that predicts 3D human motion in parallel, offering faster inference and comparable accuracy to state-of-the-art methods across multiple datasets.

Contribution

The paper proposes a novel non-autoregressive Transformer architecture for human motion prediction, leveraging spatio-temporal self-attention to improve speed and activity-agnostic performance.

Findings

Achieves better or comparable results to state-of-the-art methods.

Fewer parameters and faster inference.

Activity-agnostic and parallel prediction capability.

Abstract

3D human motion prediction is a research area of high significance and a challenge in computer vision. It is useful for the design of many applications including robotics and autonomous driving. Traditionally, autogregressive models have been used to predict human motion. However, these models have high computation needs and error accumulation that make it difficult to use them for realtime applications. In this paper, we present a non-autogressive model for human motion prediction. We focus on learning spatio-temporal representations non-autoregressively for generation of plausible future motions. We propose a novel architecture that leverages the recently proposed Transformers. Human motion involves complex spatio-temporal dynamics with joints affecting the position and rotation of each other even though they are not connected directly. The proposed model extracts these dynamics using both convolutions and the self-attention mechanism. Using specialized spatial and temporal self-attention to augment the features extracted through convolution allows our model to generate spatio-temporally coherent predictions in parallel independent of the activity. Our contributions are threefold: (i) we frame human motion prediction as a sequence-to-sequence problem and propose a non-autoregressive Transformer to forecast a sequence of poses in parallel; (ii) our method is activity agnostic; (iii) we show that despite its simplicity, our approach is able to make accurate predictions, achieving better or comparable results compared to the state-of-the-art on two public datasets, with far fewer parameters and much faster inference.