History Repeats Itself: Human Motion Prediction via Motion Attention
Abstrak
Human motion prediction aims to forecast future human poses given a past motion. Whether based on recurrent or feed-forward neural networks, existing methods fail to model the observation that human motion tends to repeat itself, even for complex sports actions and cooking activities. Here, we introduce an attention-based feed-forward network that explicitly leverages this observation. In particular, instead of modeling frame-wise attention via pose similarity, we propose to extract motion attention to capture the similarity between the current motion context and the historical motion sub-sequences. Aggregating the relevant past motions and processing the result with a graph convolutional network allows us to effectively exploit motion patterns from the long-term history to predict the future poses. Our experiments on Human3.6M, AMASS and 3DPW evidence the benefits of our approach for both periodical and non-periodical actions. Thanks to our attention model, it yields state-of-the-art results on all three datasets. Our code is available at this https URL.
Topik & Kata Kunci
Penulis (3)
Wei Mao
Miaomiao Liu
M. Salzmann
Akses Cepat
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Cek di sumber asli →- Tahun Terbit
- 2020
- Bahasa
- en
- Total Sitasi
- 348×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1007/978-3-030-58568-6_28
- Akses
- Open Access ✓