mlsanityData to 7 Oct 2026

V-VIPE: Variational View Invariant Pose Embedding

arXiv 2407.07092 ↗First posted 9 Jul 2024cs.CV cs.AIcited by 3

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Abstract (arXiv)

Learning to represent three dimensional (3D) human pose given a two dimensional (2D) image of a person, is a challenging problem. In order to make the problem less ambiguous it has become common practice to estimate 3D pose in the camera coordinate space. However, this makes the task of comparing two 3D poses difficult. In this paper, we address this challenge by separating the problem of estimating 3D pose from 2D images into two steps. We use a variational autoencoder (VAE) to find an embedding that represents 3D poses in canonical coordinate space. We refer to this embedding as variational view-invariant pose embedding V-VIPE. Using V-VIPE we can encode 2D and 3D poses and use the embedding for downstream tasks, like retrieval and classification. We can estimate 3D poses from these embeddings using the decoder as well as generate unseen 3D poses. The variability of our encoding allows it to generalize well to unseen camera views when mapping from 2D space. To the best of our knowledge, V-VIPE is the only representation to offer this diversity of applications. Code and more information can be found at https://v-vipe.github.io/.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 34 references that are in the map. First posted on arXiv 9 Jul 2024.

Builds on

23 of 34 references.

References · 23

  1. 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 493
  2. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
  3. 2016RMPE: Regional Multi-person Pose Estimationcited by 340
  4. 2020HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimationcited by 304
  5. 20213D Human Pose Estimation with Spatial and Temporal Transformerscited by 271
  6. 2017Integral Human Pose Regressioncited by 255
  7. 2016Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Posecited by 220
  8. 2018CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmarkcited by 214
  9. 2016Learning a Predictable and Generative Vector Representation for Objectscited by 173
  10. 2019Self-Supervised Learning of 3D Human Pose using Multi-view Geometrycited by 98
  11. 2019Unsupervised 3D Pose Estimation with Geometric Self-Supervisioncited by 95
  12. 2020SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approachcited by 94
11 more
  1. 20183D Human Pose Estimation in the Wild by Adversarial Learningcited by 86
  2. 2018Pose Flow: Efficient Online Pose Trackingcited by 80
  3. 2019Monocular 3D Human Pose Estimation by Generation and Ordinal Rankingcited by 76
  4. 2021Direct Multi-view Multi-person 3D Pose Estimationcited by 64
  5. 2020Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representationcited by 57
  6. 20203D Human Pose Estimation using Spatio-Temporal Networks with Explicit Occlusion Trainingcited by 54
  7. 2019View-Invariant Probabilistic Embedding for Human Posecited by 47
  8. 2021TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimationcited by 32
  9. 20163D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Informationcited by 28
  10. 2019View Invariant 3D Human Pose Estimationcited by 11
  11. 2017Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026A Study on Real-time Object Detection using Deep Learningcited by 0
  2. 2026GenCape: Structure-Inductive Generative Modeling for Category-Agnostic Pose Estimationcited by 0
  3. 2026A Plug-and-Play 2D Motion Interface for Real-World Motion Language Modelscited by 0