Dyadic Human Motion Prediction

• URL: http://arxiv.org/abs/2112.00396v1
• Date: Wed, 1 Dec 2021 10:30:40 GMT
• Title: Dyadic Human Motion Prediction
• Authors: Isinsu Katircioglu, Costa Georgantas, Mathieu Salzmann, Pascal Fua
• Abstract summary: We introduce a motion prediction framework that explicitly reasons about the interactions of two observed subjects. Specifically, we achieve this by introducing a pairwise attention mechanism that models the mutual dependencies in the motion history of the two subjects. This allows us to preserve the long-term motion dynamics in a more realistic way and more robustly predict unusual and fast-paced movements.
• Score: 119.3376964777803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prior work on human motion forecasting has mostly focused on predicting the future motion of single subjects in isolation from their past pose sequence. In the presence of closely interacting people, however, this strategy fails to account for the dependencies between the different subject's motions. In this paper, we therefore introduce a motion prediction framework that explicitly reasons about the interactions of two observed subjects. Specifically, we achieve this by introducing a pairwise attention mechanism that models the mutual dependencies in the motion history of the two subjects. This allows us to preserve the long-term motion dynamics in a more realistic way and more robustly predict unusual and fast-paced movements, such as the ones occurring in a dance scenario. To evaluate this, and because no existing motion prediction datasets depict two closely-interacting subjects, we introduce the LindyHop600K dance dataset. Our results evidence that our approach outperforms the state-of-the-art single person motion prediction techniques.

Related papers

• MDMP: Multi-modal Diffusion for supervised Motion Predictions with uncertainty [7.402769693163035]
  This paper introduces a Multi-modal Diffusion model for Motion Prediction (MDMP) It integrates skeletal data and textual descriptions of actions to generate refined long-term motion predictions with quantifiable uncertainty. Our model consistently outperforms existing generative techniques in accurately predicting long-term motions.
  arXiv  Detail & Related papers  (2024-10-04T18:49:00Z)
• SPOTR: Spatio-temporal Pose Transformers for Human Motion Prediction [12.248428883804763]
  3D human motion prediction is a research area computation of high significance and a challenge in computer vision. Traditionally, autogregressive models have been used to predict human motion. We present a non-autoregressive model for human motion prediction.
  arXiv  Detail & Related papers  (2023-03-11T01:44:29Z)
• Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction [81.94175022575966]
  We introduce the task of action-driven human motion prediction. It aims to predict multiple plausible future motions given a sequence of action labels and a short motion history.
  arXiv  Detail & Related papers  (2022-05-31T08:38:07Z)
• Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction [63.62263239934777]
  We conduct an indepth study on various pose representations with a focus on their effects on the motion prediction task. We propose a novel RNN architecture termed AHMR (Attentive Hierarchical Motion Recurrent network) for motion prediction. Our approach outperforms the state-of-the-art methods in short-term prediction and achieves much enhanced long-term prediction proficiency.
  arXiv  Detail & Related papers  (2021-12-30T10:45:22Z)
• Generating Smooth Pose Sequences for Diverse Human Motion Prediction [90.45823619796674]
  We introduce a unified deep generative network for both diverse and controllable motion prediction. Our experiments on two standard benchmark datasets, Human3.6M and HumanEva-I, demonstrate that our approach outperforms the state-of-the-art baselines in terms of both sample diversity and accuracy.
  arXiv  Detail & Related papers  (2021-08-19T00:58:00Z)
• RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting [34.54878390622877]
  We propose a generic motion forecasting framework with dynamic key information selection and ranking based on a hybrid attention mechanism. The framework is instantiated to handle multi-agent trajectory prediction and human motion forecasting tasks. We validate the framework on both synthetic simulations and motion forecasting benchmarks in different domains.
  arXiv  Detail & Related papers  (2021-08-03T06:30:30Z)
• Long Term Motion Prediction Using Keyposes [122.22758311506588]
  We argue that, to achieve long term forecasting, predicting human pose at every time instant is unnecessary. We call such poses "keyposes", and approximate complex motions by linearly interpolating between subsequent keyposes. We show that learning the sequence of such keyposes allows us to predict very long term motion, up to 5 seconds in the future.
  arXiv  Detail & Related papers  (2020-12-08T20:45:51Z)
• History Repeats Itself: Human Motion Prediction via Motion Attention [81.94175022575966]
  We introduce an attention-based feed-forward network that explicitly leverages the observation that human motion tends to repeat itself. In particular, we propose to extract motion attention to capture the similarity between the current motion context and the historical motion sub-sequences. Our experiments on Human3.6M, AMASS and 3DPW evidence the benefits of our approach for both periodical and non-periodical actions.
  arXiv  Detail & Related papers  (2020-07-23T02:12:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.