PostoMETRO: Enhancing 3D Human Mesh Recovery with Pose Token Integration

Основні поняття

Integrating pose tokens improves 3D human mesh recovery under occlusion scenarios.

Анотація

The article introduces PostoMETRO, a framework for enhancing 3D human mesh recovery by integrating pose tokens. It addresses challenges in single-image-based human mesh recovery, focusing on occlusion scenarios. By condensing 2D pose data into pose tokens and combining them with image tokens in transformers, PostoMETRO achieves robust integration of pose and image information. The method shows effectiveness on various benchmarks, demonstrating improved performance in extreme scenarios like object-occlusion and person-occlusion.

Directory:

1. Abstract

   • Recent advancements in single-image-based human mesh recovery.
   • Interest in enhancing performance under occlusion.
   • Leveraging rich 2D pose annotations for 3D reconstruction.

2. Introduction

   • Importance of 3D human pose and shape estimation.
   • Challenges in monocular camera settings.

3. Methodology: Pose Tokenizer

   • Transforming 2D poses into token sequences using VQ-VAE.
   • Training scheme for learning the pose tokenizer.

4. Overall Pipeline

   • Utilizing transformers to regress human mesh from single images.
   • Encoder-decoder architecture incorporating image and pose tokens.

5. Experimental Results

   • Performance comparisons on various datasets including object-occlusion and person-occlusion scenarios.

6. Ablation Studies

   • Analysis of different token types (image vs. pose) on model performance.
   • Impact of modulator selection (linear vs. mixer).

7. Occlusion Sensitivity Analysis

   • Per-joint breakdown of mean 3D error for occluded body parts.

8. Conclusion

Статистика

Experiments show MPVPE of 76.8mm, MPJPE of 67.7mm, PA-MPJPE of 39.8mm with HRNet-W48 backbone on 3DPW-TEST dataset.

Цитати

"In this paper, we present PostoMETRO, a novel paradigm to improve the performance of non-parametric model under occlusion scenarios."
"Our main contributions are summarized as follows: We propose PostoMETRO, a novel framework to incorporate 2D pose into transformers to help 3D human mesh estimation."

Ключові висновки, отримані з

PostoMETRO

by Wendi Yang,Z... о arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12473.pdf

Глибші Запити

How does the integration of pose tokens enhance the robustness of the model under occlusion

The integration of pose tokens enhances the robustness of the model under occlusion by providing a more structured and data-driven representation of 2D poses. By condensing 2D pose information into compact sequences of pose tokens, the model can effectively leverage this rich depiction to improve its performance in extreme scenarios like occlusion. The specialized pose tokenizer efficiently transforms 2D poses into token-wise features, which are then fed into the transformer along with image tokens. This process ensures that the model can maintain accuracy even when faced with challenging situations such as occlusion.

What are the implications of utilizing ground truth 2D pose tokens in improving model accuracy

Utilizing ground truth 2D pose tokens significantly improves model accuracy by providing precise and reliable information for training. When accurate 2D pose tokens are used as input, they enhance the quality of data fed into the transformer architecture, leading to better learning outcomes and more accurate predictions. The ground truth 2D pose tokens serve as a strong supervisory signal during training, guiding the model towards making more informed decisions and improving its overall performance on tasks such as human mesh recovery.

How can the findings from this study be applied to other areas beyond computer vision

The findings from this study have implications beyond computer vision and can be applied to various other areas where integrating different modalities or sources of information is crucial for enhancing model performance. For example:

In natural language processing (NLP), incorporating tokenized representations from multiple linguistic features could improve text generation models.
In healthcare, combining patient data from diverse sources using token-based representations could enhance diagnostic accuracy in medical imaging analysis.
In robotics, integrating sensor data through tokenization methods similar to those used in this study could lead to more robust navigation systems for autonomous vehicles.
Overall, these findings highlight the importance of leveraging multi-modal approaches across different domains to enhance model robustness and accuracy in complex tasks requiring integration of diverse sources of information.