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arxiv: 2604.22830 · v1 · submitted 2026-04-19 · 💻 cs.CV · cs.LG

Recognition: unknown

2D Pre-Training for 3D Pose Estimation

Liyao Jiang , Ruichen Chen , Keith G. Mills
Authors on Pith no claims yet

Pith reviewed 2026-05-10 06:03 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords 2D pre-training3D human pose estimationMPJPEHuman3.6MMPIIcomputer visiondeep learningOcclusion Person
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The pith

Pre-training on 2D pose data before 3D data improves accuracy and efficiency in human pose estimation over 3D data alone.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tests whether pre-training a 3D human pose estimation model on 2D datasets leads to better results when the model is later trained on 3D data. The authors extend an existing 3D HPE method so it can use extra datasets including 2D sources like MPII and 3D sources like Human3.6M and Occlusion Person. They run experiments on how factors such as model size during 2D pre-training change the final 3D performance and the model's ability to work on new datasets. The findings show that 2D pre-training beats training from 3D data by itself, with clear savings in computation time. They report an MPJPE score below 64.5 mm when combining MPII and Human3.6M.

Core claim

The central claim is that 2D pre-training consistently outperforms training on 3D data alone for 3D human pose estimation, particularly in computational efficiency, and that using MPII and Human3.6M yields MPJPE scores under 64.5 mm.

What carries the argument

An extended 3D human pose estimation scheme made compatible with additional 2D and 3D datasets, where the 2D pre-training step before 3D fine-tuning carries the performance gains.

If this is right

  • 2D pre-training reduces the computation needed to reach strong 3D pose estimation results.
  • The approach improves how well the model works on datasets it was not trained on directly.
  • Varying model size during the 2D pre-training stage changes how well the final 3D model performs.
  • The MPJPE threshold below 64.5 mm becomes reachable by combining the MPII 2D dataset with Human3.6M.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Large 2D pose datasets could reduce reliance on hard-to-collect 3D annotations for building effective models.
  • The same pre-training pattern may transfer to other tasks that estimate 3D structure from images.
  • Testing the method on more varied real-world scenes with heavy occlusion would check whether the efficiency gains hold outside controlled benchmarks.

Load-bearing premise

That expanding the 3D HPE scheme to extra 2D and 3D datasets preserves the claimed benefits without adding dataset-specific biases that would make the pre-training comparison unfair.

What would settle it

A side-by-side run of the identical model trained only on 3D data from the start that reaches the same or lower MPJPE on Human3.6M while using equal or less total compute than the 2D-pre-trained version.

Figures

Figures reproduced from arXiv: 2604.22830 by Keith G. Mills, Liyao Jiang, Ruichen Chen.

Figure 1
Figure 1. Figure 1: Example of the trained, publicly available model by [ [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Example of predicted joint annotations for a sample [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Stage 2 training plots comparing 2D PCKh@0.5 Accu [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
read the original abstract

Pre-training is a general method that is used in a range of deep learning tasks. By first training a model on one task, and then further training on the downstream task used for final evaluation, the model is forced to learn a more general understanding of the input data. While pre-training has been applied to 3D Human Pose Estimation (HPE) previously, the scope of datasets used is typically very limited to some strong benchmarks, like Human3.6M. Therefore, in this project, we expand the scope of an existing 3D HPE scheme to be compatible with additional 2D and 3D HPE datasets, like Occlusion Person. We perform an extensive study on how aspects of 2D pre-training, such as model size, affect downstream performance, and to what extent pre-training can help the model generalize to different datasets. Experimental results show that 2D pre-training consistently outperforms training on 3D data alone, particularly in terms of computational efficiency. Finally, using MPII and Human3.6M, we are able to obtain an MPJPE score of under 64.5mm.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The manuscript extends an existing 3D human pose estimation (HPE) framework to support compatibility with additional 2D datasets (e.g., MPII) and 3D datasets (e.g., Occlusion Person). It conducts an empirical study on the effects of model size, 2D pre-training, and generalization, reporting that 2D pre-training consistently outperforms 3D-only training (particularly in computational efficiency) and achieves an MPJPE below 64.5 mm on MPII combined with Human3.6M.

Significance. If the reported empirical gains hold under rigorous controls, the work demonstrates that abundant 2D pose annotations can be leveraged for pre-training to improve both accuracy and efficiency in 3D HPE, addressing data scarcity in 3D settings. This could encourage broader adoption of cross-dimensional pre-training strategies in pose estimation pipelines.

major comments (1)
  1. Experimental section: The central claim of consistent outperformance and the specific MPJPE result of under 64.5 mm lack accompanying details on baseline implementations, number of training runs, statistical significance, or error bars, which are required to substantiate robustness given the empirical nature of the contribution.
minor comments (3)
  1. Abstract and §4: The exact MPJPE value, the precise train/test split, and direct numerical comparison to the 3D-only baseline should be stated explicitly rather than summarized as 'under 64.5 mm'.
  2. §3: Notation for the expanded dataset compatibility (e.g., how 2D keypoints are mapped into the 3D loss) is introduced without a clear equation or diagram, making the pre-training procedure harder to reproduce.
  3. Related work: Prior 2D-to-3D transfer methods are mentioned only briefly; adding 2-3 key citations would better situate the contribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive assessment and recommendation for minor revision. The feedback on experimental rigor is well-taken, and we address it directly below while committing to concrete improvements in the revised manuscript.

read point-by-point responses

  1. Referee: Experimental section: The central claim of consistent outperformance and the specific MPJPE result of under 64.5 mm lack accompanying details on baseline implementations, number of training runs, statistical significance, or error bars, which are required to substantiate robustness given the empirical nature of the contribution.

    Authors: We agree that additional experimental details are necessary to support the robustness claims. The baseline implementations follow the standard protocols and architectures from the original Human3.6M and MPII papers (as referenced in Section 4), with our extensions for 2D dataset compatibility described in Section 3. The reported MPJPE of under 64.5 mm and the outperformance trends are based on the primary training configuration detailed in the experimental setup. However, the manuscript does not currently include the number of independent runs, error bars, or statistical tests. In the revision, we will add: (i) explicit descriptions of baseline re-implementations with hyperparameters, (ii) results averaged over 3 random seeds with standard deviation error bars, and (iii) paired t-test p-values comparing 2D pre-training against 3D-only training to establish significance. These additions will be placed in an expanded Section 4 and the supplementary material. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is an empirical study on 2D pre-training for 3D human pose estimation. It expands an existing scheme to additional datasets, performs ablations on model size and generalization, and reports measured outcomes such as MPJPE under 64.5 mm on MPII + Human3.6M. No derivation chain, equations, fitted parameters renamed as predictions, or self-citation load-bearing uniqueness theorems appear in the provided text or abstract. All central claims rest on direct experimental comparisons rather than quantities defined by the model itself, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical derivations, free parameters, axioms, or invented entities are described; the paper is an empirical machine-learning study whose claims rest on experimental measurements.

pith-pipeline@v0.9.0 · 5504 in / 1205 out tokens · 31636 ms · 2026-05-10T06:03:51.547543+00:00 · methodology

discussion (0)

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