Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers

• URL: http://arxiv.org/abs/2109.07531v1
• Date: Wed, 15 Sep 2021 18:55:15 GMT
• Title: Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers
• Authors: Angel Mart\'inez-Gonz\'alez, Michael Villamizar, Jean-Marc Odobez
• Abstract summary: We propose to leverage Transformer architectures for non-autoregressive human motion prediction. Our approach decodes elements in parallel from a query sequence, instead of conditioning on previous predictions. We show that despite its simplicity, our approach achieves competitive results in two public datasets.
• Score: 24.36592204215444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose to leverage Transformer architectures for non-autoregressive human motion prediction. Our approach decodes elements in parallel from a query sequence, instead of conditioning on previous predictions such as instate-of-the-art RNN-based approaches. In such a way our approach is less computational intensive and potentially avoids error accumulation to long term elements in the sequence. In that context, our contributions are fourfold: (i) we frame human motion prediction as a sequence-to-sequence problem and propose a non-autoregressive Transformer to infer the sequences of poses in parallel; (ii) we propose to decode sequences of 3D poses from a query sequence generated in advance with elements from the input sequence;(iii) we propose to perform skeleton-based activity classification from the encoder memory, in the hope that identifying the activity can improve predictions;(iv) we show that despite its simplicity, our approach achieves competitive results in two public datasets, although surprisingly more for short term predictions rather than for long term ones.

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