# Reconstructing Hands in 3D with Transformers

**Authors:** Georgios Pavlakos, Dandan Shan, Ilija Radosavovic, Angjoo Kanazawa, David Fouhey, Jitendra Malik  
**arXiv:** [2312.05251](https://arxiv.org/abs/2312.05251)  
**Date:** 2023-12-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2312.05251v1/

## Key points

- Paper titled "Reconstructing Hands in 3D with Transformers" by Georgios Pavlakos, Dandan Shan, Ilija Radosavovic, Angjoo Kanazawa, David Fouhey, and Jitendra Malik
- Introduces innovative approach HaMeR for reconstructing hands in 3D from monocular input using a fully transformer-based architecture
- Success attributed to scaling up training data and deep network capacity for increased accuracy and robustness
- Utilizes large-scale Vision Transformer architecture that outperforms existing baselines on popular 3D hand pose benchmarks
- Combines multiple datasets with 2D or 3D hand annotations for model training
- Introduces new benchmark dataset HInt capturing diverse hand poses from ego-centric views or YouTube videos
- Demonstrates significant improvements over previous methods in handling heavy occlusion and interactions with objects or other hands
- Code, data, and models available for further exploration and implementation by the research community
- Precision and robustness of HaMeR make it suitable for applications like robotics, action recognition, and sign language understanding

## Summary

In their paper titled "Reconstructing Hands in 3D with Transformers," Georgios Pavlakos, Dandan Shan, Ilija Radosavovic, Angjoo Kanazawa, David Fouhey, and Jitendra Malik present an innovative approach for reconstructing hands in 3D from monocular input. Their method, known as Hand Mesh Recovery (HaMeR), utilizes a fully transformer-based architecture to analyze hands with significantly increased accuracy and robustness compared to previous techniques. The success of HaMeR is attributed to scaling up both the training data and the capacity of the deep network for hand reconstruction. has been a challenging task due to the complexity and variability of hand poses. However, HaMeR's  proves to be effective in accurately reconstructing hands from monocular input. This is achieved by leveraging a large-scale Vision Transformer architecture that consistently outperforms existing baselines on popular 3D hand pose benchmarks. To train their model, the researchers combine multiple datasets containing 2D or 3D hand annotations. They employ a large-scale Vision Transformer architecture to build a  that consistently outperforms existing baselines on popular 3D hand pose benchmarks. Furthermore, they annotate in-the-wild datasets with 2D hand keypoint annotations to evaluate the effectiveness of their design in non-controlled settings. The team introduces a new benchmark dataset called HInt which includes diverse hand poses captured from ego-centric views or YouTube videos. By evaluating their approach on this challenging dataset, they demonstrate significant improvements over previous methods. The researchers showcase qualitative results of HaMeR on various datasets such as New Days, VISOR, Ego4D, and Internet images. They highlight that HaMeR excels in handling cases with heavy occlusion and interactions with objects or other hands. The authors hope that the precision and robustness of their approach will spark interest in utilizing their system for applications where accurate 3D hand estimation is crucial, including robotics, action recognition, and sign language understanding. They have made their code, data, and models available on their project website for further exploration and implementation by the research community. and  are key features of HaMeR that make it a promising tool for various real-world applications.