CrossFormer: Cross Spatio-Temporal Transformer for 3D Human Pose Estimation

• URL: http://arxiv.org/abs/2203.13387v1
• Date: Thu, 24 Mar 2022 23:40:11 GMT
• Title: CrossFormer: Cross Spatio-Temporal Transformer for 3D Human Pose Estimation
• Authors: Mohammed Hassanin, Abdelwahed Khamiss, Mohammed Bennamoun, Farid Boussaid, and Ibrahim Radwan
• Abstract summary: 3D human pose estimation can be handled by encoding the geometric dependencies between the body parts and enforcing the kinematic constraints. Recent Transformer has been adopted to encode the long-range dependencies between the joints in the spatial and temporal domains. We propose a novel pose estimation Transformer featuring rich representations of body joints critical for capturing subtle changes across frames.
• Score: 24.08170512746056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D human pose estimation can be handled by encoding the geometric dependencies between the body parts and enforcing the kinematic constraints. Recently, Transformer has been adopted to encode the long-range dependencies between the joints in the spatial and temporal domains. While they had shown excellence in long-range dependencies, studies have noted the need for improving the locality of vision Transformers. In this direction, we propose a novel pose estimation Transformer featuring rich representations of body joints critical for capturing subtle changes across frames (i.e., inter-feature representation). Specifically, through two novel interaction modules; Cross-Joint Interaction and Cross-Frame Interaction, the model explicitly encodes the local and global dependencies between the body joints. The proposed architecture achieved state-of-the-art performance on two popular 3D human pose estimation datasets, Human3.6 and MPI-INF-3DHP. In particular, our proposed CrossFormer method boosts performance by 0.9% and 0.3%, compared to the closest counterpart, PoseFormer, using the detected 2D poses and ground-truth settings respectively.

Related papers

• Graph and Skipped Transformer: Exploiting Spatial and Temporal Modeling Capacities for Efficient 3D Human Pose Estimation [36.93661496405653]
  We take a global approach to exploit Transformer-temporal information with a concise Graph and Skipped Transformer architecture. Specifically, in 3D pose stage, coarse-grained body parts are deployed to construct a fully data-driven adaptive model. Experiments are conducted on Human3.6M, MPI-INF-3DHP and Human-Eva benchmarks.
  arXiv  Detail & Related papers  (2024-07-03T10:42:09Z)
• 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)
• Double-chain Constraints for 3D Human Pose Estimation in Images and Videos [21.42410292863492]
  Reconstructing 3D poses from 2D poses lacking depth information is challenging due to the complexity and diversity of human motion. We propose a novel model, called Double-chain Graph Convolutional Transformer (DC-GCT), to constrain the pose. We show that DC-GCT achieves state-of-the-art performance on two challenging datasets.
  arXiv  Detail & Related papers  (2023-08-10T02:41:18Z)
• Global-to-Local Modeling for Video-based 3D Human Pose and Shape Estimation [53.04781510348416]
  Video-based 3D human pose and shape estimations are evaluated by intra-frame accuracy and inter-frame smoothness. We propose to structurally decouple the modeling of long-term and short-term correlations in an end-to-end framework, Global-to-Local Transformer (GLoT) Our GLoT surpasses previous state-of-the-art methods with the lowest model parameters on popular benchmarks, i.e., 3DPW, MPI-INF-3DHP, and Human3.6M.
  arXiv  Detail & Related papers  (2023-03-26T14:57:49Z)
• (Fusionformer):Exploiting the Joint Motion Synergy with Fusion Network Based On Transformer for 3D Human Pose Estimation [1.52292571922932]
  Many previous methods lack the understanding of local joint information.cite8888987considers the temporal relationship of a single joint in this work. Our proposed textbfFusionformer method introduces a global-temporal self-trajectory module and a cross-temporal self-trajectory module. The results show an improvement of 2.4% MPJPE and 4.3% P-MPJPE on the Human3.6M dataset.
  arXiv  Detail & Related papers  (2022-10-08T12:22:10Z)
• Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation [63.199549837604444]
  3D human pose estimation approaches leverage different forms of strong (2D/3D pose) or weak (multi-view or depth) paired supervision. We cast 3D pose learning as a self-supervised adaptation problem that aims to transfer the task knowledge from a labeled source domain to a completely unpaired target. We evaluate different self-adaptation settings and demonstrate state-of-the-art 3D human pose estimation performance on standard benchmarks.
  arXiv  Detail & Related papers  (2022-04-05T03:52:57Z)
• MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video [75.23812405203778]
  Recent solutions have been introduced to estimate 3D human pose from 2D keypoint sequence by considering body joints among all frames globally to learn-temporal correlation. We propose Mix Mix, which has temporal transformer block to separately model the temporal motion of each joint and a transformer block inter-joint spatial correlation. In addition, the network output is extended from the central frame to entire frames of input video, improving the coherence between the input and output benchmarks.
  arXiv  Detail & Related papers  (2022-03-02T04:20:59Z)
• 3D Human Pose Estimation with Spatial and Temporal Transformers [59.433208652418976]
  We present PoseFormer, a purely transformer-based approach for 3D human pose estimation in videos. Inspired by recent developments in vision transformers, we design a spatial-temporal transformer structure. We quantitatively and qualitatively evaluate our method on two popular and standard benchmark datasets.
  arXiv  Detail & Related papers  (2021-03-18T18:14:37Z)

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.