3D Human Motion Estimation via Motion Compression and Refinement

• URL: http://arxiv.org/abs/2008.03789v2
• Date: Mon, 5 Oct 2020 20:24:59 GMT
• Title: 3D Human Motion Estimation via Motion Compression and Refinement
• Authors: Zhengyi Luo, S. Alireza Golestaneh, Kris M. Kitani
• Abstract summary: We develop a technique for generating smooth and accurate 3D human pose and motion estimates from RGB video sequences. Our method, which we call Motion Estimation via Variational Autoencoder (MEVA), decomposes a temporal sequence of human motion into a smooth motion representation.
• Score: 27.49664453166726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop a technique for generating smooth and accurate 3D human pose and motion estimates from RGB video sequences. Our method, which we call Motion Estimation via Variational Autoencoder (MEVA), decomposes a temporal sequence of human motion into a smooth motion representation using auto-encoder-based motion compression and a residual representation learned through motion refinement. This two-step encoding of human motion captures human motion in two stages: a general human motion estimation step that captures the coarse overall motion, and a residual estimation that adds back person-specific motion details. Experiments show that our method produces both smooth and accurate 3D human pose and motion estimates.

Related papers

• 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)
• COIN: Control-Inpainting Diffusion Prior for Human and Camera Motion Estimation [98.05046790227561]
  COIN is a control-inpainting motion diffusion prior that enables fine-grained control to disentangle human and camera motions. COIN outperforms the state-of-the-art methods in terms of global human motion estimation and camera motion estimation.
  arXiv  Detail & Related papers  (2024-08-29T10:36:29Z)
• Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations [61.659439423703155]
  TOHO: Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations. Our method generates continuous motions that are parameterized only by the temporal coordinate. This work takes a step further toward general human-scene interaction simulation.
  arXiv  Detail & Related papers  (2023-03-23T09:31:56Z)
• MotionBERT: A Unified Perspective on Learning Human Motion Representations [46.67364057245364]
  We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources. We propose a pretraining stage in which a motion encoder is trained to recover the underlying 3D motion from noisy partial 2D observations. We implement motion encoder with a Dual-stream Spatio-temporal Transformer (DSTformer) neural network.
  arXiv  Detail & Related papers  (2022-10-12T19:46:25Z)
• Action2video: Generating Videos of Human 3D Actions [31.665831044217363]
  We aim to tackle the interesting yet challenging problem of generating videos of diverse and natural human motions from prescribed action categories. Key issue lies in the ability to synthesize multiple distinct motion sequences that are realistic in their visual appearances. Action2motionally generates plausible 3D pose sequences of a prescribed action category, which are processed and rendered by motion2video to form 2D videos.
  arXiv  Detail & Related papers  (2021-11-12T20:20:37Z)
• HuMoR: 3D Human Motion Model for Robust Pose Estimation [100.55369985297797]
  HuMoR is a 3D Human Motion Model for Robust Estimation of temporal pose and shape. We introduce a conditional variational autoencoder, which learns a distribution of the change in pose at each step of a motion sequence. We demonstrate that our model generalizes to diverse motions and body shapes after training on a large motion capture dataset.
  arXiv  Detail & Related papers  (2021-05-10T21:04:55Z)
• Action2Motion: Conditioned Generation of 3D Human Motions [28.031644518303075]
  We aim to generateplausible human motion sequences in 3D. Each sampled sequence faithfully resembles anaturalhuman bodyarticulation dynamics. A new 3D human motion dataset, HumanAct12, is also constructed.
  arXiv  Detail & Related papers  (2020-07-30T05:29:59Z)
• Contact and Human Dynamics from Monocular Video [73.47466545178396]
  Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors. We present a physics-based method for inferring 3D human motion from video sequences that takes initial 2D and 3D pose estimates as input.
  arXiv  Detail & Related papers  (2020-07-22T21:09:11Z)
• Motion Guided 3D Pose Estimation from Videos [81.14443206968444]
  We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose. In computing motion loss, a simple yet effective representation for keypoint motion, called pairwise motion encoding, is introduced. We design a new graph convolutional network architecture, U-shaped GCN (UGCN), which captures both short-term and long-term motion information.
  arXiv  Detail & Related papers  (2020-04-29T06:59:30Z)

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.