mlsanityData to 14 Oct 2026

TAPE: Temporal Attention-based Probabilistic human pose and shape Estimation

arXiv 2305.00181 ↗First posted 29 Apr 2023cs.CVcited by 1

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Abstract (arXiv)

Reconstructing 3D human pose and shape from monocular videos is a well-studied but challenging problem. Common challenges include occlusions, the inherent ambiguities in the 2D to 3D mapping and the computational complexity of video processing. Existing methods ignore the ambiguities of the reconstruction and provide a single deterministic estimate for the 3D pose. In order to address these issues, we present a Temporal Attention based Probabilistic human pose and shape Estimation method (TAPE) that operates on an RGB video. More specifically, we propose to use a neural network to encode video frames to temporal features using an attention-based neural network. Given these features, we output a per-frame but temporally-informed probability distribution for the human pose using Normalizing Flows. We show that TAPE outperforms state-of-the-art methods in standard benchmarks and serves as an effective video-based prior for optimization-based human pose and shape estimation. Code is available at: https: //github.com/nikosvasilik/TAPE

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 36 references that are in the map. First posted on arXiv 29 Apr 2023.

Builds on

28 of 36 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019Expressive Body Capture: 3D Hands, Face, and Body from a Single Imagecited by 1,612
  3. 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,236
  4. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,214
  5. 2017End-to-end Recovery of Human Shape and Posecited by 885
  6. 2016Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Imagecited by 695
  7. 2019Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loopcited by 627
  8. 2019VIBE: Video Inference for Human Body Pose and Shape Estimationcited by 620
  9. 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 494
  10. 2020End-to-End Human Pose and Mesh Reconstruction with Transformerscited by 418
  11. 2016Convolutional Pose Machinescited by 325
  12. 2018Learning 3D Human Dynamics from Videocited by 316
16 more
  1. 2019Convolutional Mesh Regression for Single-Image Human Shape Reconstructioncited by 270
  2. 2021Mesh Graphormercited by 265
  3. 2017Unite the People: Closing the Loop Between 3D and 2D Human Representationscited by 205
  4. 2018Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimationcited by 185
  5. 2020Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Videocited by 163
  6. 2018Monocular Total Capture: Posing Face, Body, and Hands in the Wildcited by 162
  7. 2020Monocular Expressive Body Regression through Body-Driven Attentioncited by 155
  8. 2021Probabilistic Modeling for Human Mesh Recoverycited by 153
  9. 2020Coherent Reconstruction of Multiple Humans from a Single Imagecited by 136
  10. 2020Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimationcited by 113
  11. 2021On Self-Contact and Human Posecited by 89
  12. 2020Hierarchical Kinematic Human Mesh Recoverycited by 71
  13. 2022Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape Estimation from Monocular Videocited by 60
  14. 2019TexturePose: Supervising Human Mesh Estimation with Texture Consistencycited by 53
  15. 20203D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Datacited by 50
  16. 2021Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wildcited by 48

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024D-PoSE: Depth as an Intermediate Representation for 3D Human Pose and Shape Estimationcited by 1