SDMTL: Semi-Decoupled Multi-grained Trajectory Learning for 3D human motion prediction

• URL: http://arxiv.org/abs/2010.05133v1
• Date: Sun, 11 Oct 2020 01:29:21 GMT
• Title: SDMTL: Semi-Decoupled Multi-grained Trajectory Learning for 3D human motion prediction
• Authors: Xiaoli Liu and Jianqin Yin
• Abstract summary: We propose a novel end-to-end network, Semi-Decoupled Multi-grained Trajectory Learning network, to predict future human motion. Specifically, we capture the temporal dynamics of motion trajectory at multi-granularity, including fine granularity and coarse. We learn multi-grained trajectory information using BSMEs hierarchically and further capture the information of temporal evolutional directions at each granularity.
• Score: 5.581663772616127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting future human motion is critical for intelligent robots to interact with humans in the real world, and human motion has the nature of multi-granularity. However, most of the existing work either implicitly modeled multi-granularity information via fixed modes or focused on modeling a single granularity, making it hard to well capture this nature for accurate predictions. In contrast, we propose a novel end-to-end network, Semi-Decoupled Multi-grained Trajectory Learning network (SDMTL), to predict future poses, which not only flexibly captures rich multi-grained trajectory information but also aggregates multi-granularity information for predictions. Specifically, we first introduce a Brain-inspired Semi-decoupled Motion-sensitive Encoding module (BSME), effectively capturing spatiotemporal features in a semi-decoupled manner. Then, we capture the temporal dynamics of motion trajectory at multi-granularity, including fine granularity and coarse granularity. We learn multi-grained trajectory information using BSMEs hierarchically and further capture the information of temporal evolutional directions at each granularity by gathering the outputs of BSMEs at each granularity and applying temporal convolutions along the motion trajectory. Next, the captured motion dynamics can be further enhanced by aggregating the information of multi-granularity with a weighted summation scheme. Finally, experimental results on two benchmarks, including Human3.6M and CMU-Mocap, show that our method achieves state-of-the-art performance, demonstrating the effectiveness of our proposed method. The code will be available if the paper is accepted.

Related papers

• Multi-Transmotion: Pre-trained Model for Human Motion Prediction [68.87010221355223]
  Multi-Transmotion is an innovative transformer-based model designed for cross-modality pre-training. Our methodology demonstrates competitive performance across various datasets on several downstream tasks.
  arXiv  Detail & Related papers  (2024-11-04T23:15:21Z)
• 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)
• MoManifold: Learning to Measure 3D Human Motion via Decoupled Joint Acceleration Manifolds [20.83684434910106]
  We present MoManifold, a novel human motion prior, which models plausible human motion in continuous high-dimensional motion space. Specifically, we propose novel decoupled joint acceleration to model human dynamics from existing limited motion data. Extensive experiments demonstrate that MoManifold outperforms existing SOTAs as a prior in several downstream tasks.
  arXiv  Detail & Related papers  (2024-09-01T15:00:16Z)
• DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion [70.33381660741861]
  We present DiverseMotion, a new approach for synthesizing high-quality human motions conditioned on textual descriptions. We show that our DiverseMotion achieves the state-of-the-art motion quality and competitive motion diversity.
  arXiv  Detail & Related papers  (2023-09-04T05:43:48Z)
• Spatio-Temporal Branching for Motion Prediction using Motion Increments [55.68088298632865]
  Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications. Traditional methods rely on hand-crafted features and machine learning techniques. We propose a noveltemporal-temporal branching network using incremental information for HMP.
  arXiv  Detail & Related papers  (2023-08-02T12:04:28Z)
• MotionTrack: Learning Motion Predictor for Multiple Object Tracking [68.68339102749358]
  We introduce a novel motion-based tracker, MotionTrack, centered around a learnable motion predictor. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on datasets such as Dancetrack and SportsMOT.
  arXiv  Detail & Related papers  (2023-06-05T04:24:11Z)
• Transformer Inertial Poser: Attention-based Real-time Human Motion Reconstruction from Sparse IMUs [79.72586714047199]
  We propose an attention-based deep learning method to reconstruct full-body motion from six IMU sensors in real-time. Our method achieves new state-of-the-art results both quantitatively and qualitatively, while being simple to implement and smaller in size.
  arXiv  Detail & Related papers  (2022-03-29T16:24:52Z)
• Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks [9.06677862854201]
  We propose a novel approach to predict future human motions from a single image, with mixture density networks (MDN) modeling. Contrary to most existing deep human motion prediction approaches, the multimodal nature of MDN enables the generation of diverse future motion hypotheses. Our trained model directly takes an image as input and generates multiple plausible motions that satisfy the given condition.
  arXiv  Detail & Related papers  (2021-09-13T08:49:33Z)
• Multi-grained Trajectory Graph Convolutional Networks for Habit-unrelated Human Motion Prediction [4.070072825448614]
  A multigrained graph convolutional networks based lightweight framework is proposed for habit-unrelated human motion prediction. A new motion generation method is proposed to generate the motion with left-handedness, to better model the motion with less bias to the human habit. Experimental results on challenging datasets, including Humantemporal3.6M and CMU Mocap, show that the proposed model outperforms state-of-the-art with less than 0.12 times parameters.
  arXiv  Detail & Related papers  (2020-12-23T09:41:50Z)
• Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision [27.468166556263256]
  We propose a temporal pyramid network for pedestrian trajectory prediction through a squeeze modulation and a dilation modulation. Our hierarchical framework builds a feature pyramid with increasingly richer temporal information from top to bottom, which can better capture the motion behavior at various tempos. By progressively merging the top coarse features of global context to the bottom fine features of rich local context, our method can fully exploit both the long-range and short-range information of the trajectory.
  arXiv  Detail & Related papers  (2020-12-03T13:02:59Z)
• Motion Prediction Using Temporal Inception Module [96.76721173517895]
  We propose a Temporal Inception Module (TIM) to encode human motion. Our framework produces input embeddings using convolutional layers, by using different kernel sizes for different input lengths. The experimental results on standard motion prediction benchmark datasets Human3.6M and CMU motion capture dataset show that our approach consistently outperforms the state of the art methods.
  arXiv  Detail & Related papers  (2020-10-06T20:26:01Z)

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.