# TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representation

**Authors:** Sai Kumar Dwivedi, Yu Sun, Priyanka Patel, Yao Feng, Michael J. Black  
**arXiv:** [2404.16752](https://arxiv.org/abs/2404.16752)  
**Date:** 2024-04-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.16752v1/

## Key points

- Authors address the challenge of regressing 3D human pose and shape from a single image with a focus on achieving high 3D accuracy
- Observation that as 2D accuracy increases, there is a decline in 3D pose accuracy due to biases in pseudo-ground-truth data and camera projection model
- Introduction of Threshold-Adaptive Loss Scaling (TALS) to penalize significant errors in 2D and pseudo-ground-truth data without affecting smaller errors
- Proposal of tokenized representations of human pose and formulating the problem as token prediction to reduce ambiguity in estimating valid human poses
- Extensive experiments demonstrate that the reformulated keypoint loss function and tokenization technique significantly improve 3D accuracy compared to existing methods

## Summary

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In their paper "TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representation," authors Sai Kumar Dwivedi, Yu Sun, Priyanka Patel, Yao Feng, and Michael J. Black address the challenging task of regressing 3D human pose and shape from a single image while focusing on achieving high 3D accuracy. The current state-of-the-art methods rely on large datasets of 3D pseudo-ground-truth (p-GT) and 2D keypoints to achieve robust performance. However, the authors make an intriguing observation that as 2D accuracy increases, there is a paradoxical decline in 3D pose accuracy. This phenomenon is attributed to biases present in the p-GT data and the utilization of an approximate camera projection model. To address this issue, the authors conduct a thorough analysis to quantify the error introduced by existing camera models and demonstrate that accurately fitting 2D keypoints and p-GT can lead to incorrect 3D poses. They define specific invalid distances within which minimizing losses related to 2D keypoints and p-GT becomes detrimental. To mitigate this problem, they propose a novel loss function called Threshold-Adaptive Loss Scaling (TALS), which penalizes significant errors in 2D and p-GT data without affecting smaller errors. Furthermore, the paper discusses the challenge of reducing ambiguity in estimating valid human poses based on given evidence. While prior knowledge about valid poses can introduce bias, the authors propose a solution by leveraging tokenized representations of human pose and formulating the problem as token prediction. This approach effectively restricts estimated poses to a space of valid configurations, providing a uniform prior without introducing unwanted biases. Extensive experiments conducted on datasets such as EMDB and 3DPW demonstrate that the reformulated keypoint loss function and tokenization technique enable training on diverse real-world data while significantly improving 3D accuracy compared to existing state-of-the-art methods. The authors make their models and code available for further research at https://tokenhmr.is.tue.mpg.de. Overall, this work presents innovative advancements in human mesh recovery by addressing key challenges in regressing accurate 3D human pose from single images through novel loss functions and tokenized pose representations.