Self-Attentive 3D Human Pose and Shape Estimation from Videos

• URL: http://arxiv.org/abs/2103.14182v1
• Date: Fri, 26 Mar 2021 00:02:19 GMT
• Title: Self-Attentive 3D Human Pose and Shape Estimation from Videos
• Authors: Yun-Chun Chen, Marco Piccirilli, Robinson Piramuthu, Ming-Hsuan Yang
• Abstract summary: We present a video-based learning algorithm for 3D human pose and shape estimation. We exploit temporal information in videos and propose a self-attention module. We evaluate our method on the 3DPW, MPI-INF-3DHP, and Human3.6M datasets.
• Score: 82.63503361008607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the task of estimating 3D human pose and shape from videos. While existing frame-based approaches have made significant progress, these methods are independently applied to each image, thereby often leading to inconsistent predictions. In this work, we present a video-based learning algorithm for 3D human pose and shape estimation. The key insights of our method are two-fold. First, to address the inconsistent temporal prediction issue, we exploit temporal information in videos and propose a self-attention module that jointly considers short-range and long-range dependencies across frames, resulting in temporally coherent estimations. Second, we model human motion with a forecasting module that allows the transition between adjacent frames to be smooth. We evaluate our method on the 3DPW, MPI-INF-3DHP, and Human3.6M datasets. Extensive experimental results show that our algorithm performs favorably against the state-of-the-art methods.

Related papers

• STGFormer: Spatio-Temporal GraphFormer for 3D Human Pose Estimation in Video [7.345621536750547]
  This paper presents a graph-based framework for 3D human pose estimation in video. Specifically, we develop a graph-based attention mechanism, integrating graph information directly into the respective attention layers. We demonstrate that our method achieves significant stateof-the-art performance in 3D human pose estimation.
  arXiv  Detail & Related papers  (2024-07-14T06:45:27Z)
• UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues [55.69339788566899]
  UPose3D is a novel approach for multi-view 3D human pose estimation. It improves robustness and flexibility without requiring direct 3D annotations.
  arXiv  Detail & Related papers  (2024-04-23T00:18:00Z)
• SkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers [57.46911575980854]
  We introduce SkelFormer, a novel markerless motion capture pipeline for multi-view human pose and shape estimation. Our method first uses off-the-shelf 2D keypoint estimators, pre-trained on large-scale in-the-wild data, to obtain 3D joint positions. Next, we design a regression-based inverse-kinematic skeletal transformer that maps the joint positions to pose and shape representations from heavily noisy observations.
  arXiv  Detail & Related papers  (2024-04-19T04:51:18Z)
• Unsupervised 3D Pose Estimation with Non-Rigid Structure-from-Motion Modeling [83.76377808476039]
  We propose a new modeling method for human pose deformations and design an accompanying diffusion-based motion prior. Inspired by the field of non-rigid structure-from-motion, we divide the task of reconstructing 3D human skeletons in motion into the estimation of a 3D reference skeleton. A mixed spatial-temporal NRSfMformer is used to simultaneously estimate the 3D reference skeleton and the skeleton deformation of each frame from 2D observations sequence.
  arXiv  Detail & Related papers  (2023-08-18T16:41:57Z)
• Kinematic-aware Hierarchical Attention Network for Human Pose Estimation in Videos [17.831839654593452]
  Previous-based human pose estimation methods have shown promising results by leveraging features of consecutive frames. Most approaches compromise accuracy to jitter and do not comprehend the temporal aspects of human motion. We design an architecture that exploits kinematic keypoint features.
  arXiv  Detail & Related papers  (2022-11-29T01:46:11Z)
• A generic diffusion-based approach for 3D human pose prediction in the wild [68.00961210467479]
  3D human pose forecasting, i.e., predicting a sequence of future human 3D poses given a sequence of past observed ones, is a challenging-temporal task. We provide a unified formulation in which incomplete elements (no matter in the prediction or observation) are treated as noise and propose a conditional diffusion model that denoises them and forecasts plausible poses. We investigate our findings on four standard datasets and obtain significant improvements over the state-of-the-art.
  arXiv  Detail & Related papers  (2022-10-11T17:59:54Z)
• Live Stream Temporally Embedded 3D Human Body Pose and Shape Estimation [13.40702053084305]
  We present a temporally embedded 3D human body pose and shape estimation (TePose) method to improve the accuracy and temporal consistency pose in live stream videos. A multi-scale convolutional network is presented as the motion discriminator for adversarial training using datasets without any 3D labeling.
  arXiv  Detail & Related papers  (2022-07-25T21:21:59Z)
• Learning Temporal 3D Human Pose Estimation with Pseudo-Labels [3.0954251281114513]
  We present a simple, yet effective, approach for self-supervised 3D human pose estimation. We rely on triangulating 2D body pose estimates of a multiple-view camera system. Our method achieves state-of-the-art performance in the Human3.6M and MPI-INF-3DHP benchmarks.
  arXiv  Detail & Related papers  (2021-10-14T17:40:45Z)
• Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis [72.34794624243281]
  We propose a self-supervised learning framework to disentangle variations from unlabeled video frames. Our differentiable formalization, bridging the representation gap between the 3D pose and spatial part maps, allows us to operate on videos with diverse camera movements.
  arXiv  Detail & Related papers  (2020-04-09T07:55:01Z)

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.