MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning

• URL: http://arxiv.org/abs/2411.16964v1
• Date: Mon, 25 Nov 2024 22:09:19 GMT
• Title: MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning
• Authors: Yuming Feng, Zhiyang Dou, Ling-Hao Chen, Yuan Liu, Tianyu Li, Jingbo Wang, Zeyu Cao, Wenping Wang, Taku Komura, Lingjie Liu,
• Abstract summary: MotionWavelet is a human motion prediction framework that studies human motion patterns in the spatial-frequency domain. Wavelet Diffusion Model learns a Wavelet Manifold by applying Wavelet Transformation on the motion data. Wavelet Space Shaping Guidance mechanism refines the denoising process to improve conformity with the manifold structure.
• Score: 57.078168638373874
• License:
• Abstract: Modeling temporal characteristics and the non-stationary dynamics of body movement plays a significant role in predicting human future motions. However, it is challenging to capture these features due to the subtle transitions involved in the complex human motions. This paper introduces MotionWavelet, a human motion prediction framework that utilizes Wavelet Transformation and studies human motion patterns in the spatial-frequency domain. In MotionWavelet, a Wavelet Diffusion Model (WDM) learns a Wavelet Manifold by applying Wavelet Transformation on the motion data therefore encoding the intricate spatial and temporal motion patterns. Once the Wavelet Manifold is built, WDM trains a diffusion model to generate human motions from Wavelet latent vectors. In addition to the WDM, MotionWavelet also presents a Wavelet Space Shaping Guidance mechanism to refine the denoising process to improve conformity with the manifold structure. WDM also develops Temporal Attention-Based Guidance to enhance prediction accuracy. Extensive experiments validate the effectiveness of MotionWavelet, demonstrating improved prediction accuracy and enhanced generalization across various benchmarks. Our code and models will be released upon acceptance.

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)
• DivDiff: A Conditional Diffusion Model for Diverse Human Motion Prediction [9.447439259813112]
  We propose a conditional diffusion-based generative model, called DivDiff, to predict more diverse and realistic human motions. Specifically, the DivDiff employs DDPM as our backbone and incorporates Discrete Cosine Transform (DCT) and transformer mechanisms. We design a diversified reinforcement sampling function (DRSF) to enforce human skeletal constraints on the predicted human motions.
  arXiv  Detail & Related papers  (2024-08-16T04:51:32Z)
• Spectral Motion Alignment for Video Motion Transfer using Diffusion Models [54.32923808964701]
  Spectral Motion Alignment (SMA) is a framework that refines and aligns motion vectors using Fourier and wavelet transforms. SMA learns motion patterns by incorporating frequency-domain regularization, facilitating the learning of whole-frame global motion dynamics. Extensive experiments demonstrate SMA's efficacy in improving motion transfer while maintaining computational efficiency and compatibility across various video customization frameworks.
  arXiv  Detail & Related papers  (2024-03-22T14:47:18Z)
• TransFusion: A Practical and Effective Transformer-based Diffusion Model for 3D Human Motion Prediction [1.8923948104852863]
  We propose TransFusion, an innovative and practical diffusion-based model for 3D human motion prediction. Our model leverages Transformer as the backbone with long skip connections between shallow and deep layers. In contrast to prior diffusion-based models that utilize extra modules like cross-attention and adaptive layer normalization, we treat all inputs, including conditions, as tokens to create a more lightweight model.
  arXiv  Detail & Related papers  (2023-07-30T01:52:07Z)
• Towards Accurate Human Motion Prediction via Iterative Refinement [9.910719309846128]
  FreqMRN takes into account both the kinematic structure of the human body and the temporal smoothness nature of motion. We evaluate FreqMRN on several standard benchmark datasets, including Human3.6M, AMASS and 3DPW.
  arXiv  Detail & Related papers  (2023-05-08T03:43:51Z)
• Executing your Commands via Motion Diffusion in Latent Space [51.64652463205012]
  We propose a Motion Latent-based Diffusion model (MLD) to produce vivid motion sequences conforming to the given conditional inputs. Our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks.
  arXiv  Detail & Related papers  (2022-12-08T03:07:00Z)
• PhysDiff: Physics-Guided Human Motion Diffusion Model [101.1823574561535]
  Existing motion diffusion models largely disregard the laws of physics in the diffusion process. PhysDiff incorporates physical constraints into the diffusion process. Our approach achieves state-of-the-art motion quality and improves physical plausibility drastically.
  arXiv  Detail & Related papers  (2022-12-05T18:59:52Z)
• MotionAug: Augmentation with Physical Correction for Human Motion Prediction [19.240717471864723]
  This paper presents a motion data augmentation scheme incorporating motion synthesis encouraging diversity and motion correction imposing physical plausibility. Our method outperforms previous noise-based motion augmentation methods by a large margin on both Recurrent Neural Network-based and Graph Convolutional Network-based human motion prediction models.
  arXiv  Detail & Related papers  (2022-03-17T06:53:15Z)
• 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)
• 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.