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Lai Chun-Yu

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Proceedings Article | 5 February 2025 Paper

Human 3D mesh reconstruction from single RGB image based on deep learning network

Wen-Nung Lie, Chun-Yu Lai, Jui-Chiu Chiang, Po-Han Huang

Proceedings Volume 13510, 1351020 (2025) https://doi.org/10.1117/12.3058110

KEYWORDS: 3D modeling, RGB color model, 3D image reconstruction, Data transmission, Image restoration, Convolution, Virtual reality, Transformers, Feature extraction, 3D image processing

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In recent years, with the rapid development of the artificial intelligence technology, many computer vision applications have been proposed, including Virtual Reality (VR), Augmented Reality (AR), and the metaverse. One of the key technologies to realize these vision applications is "human 3D mesh reconstruction." This paper aims to research the task of reconstructing human 3D mesh model from a single RGB image, with a focus on achieving good reconstruction results while reducing computational costs, thereby establishing advantages for future daily life applications. We propose an improved version of the HyperGraph Convolution Network (HGCN), called the Swift HyperGraph Convolution Network (Swift-HGCN), which allows for faster transmission of information across different parts of the human mesh model. Additionally, we apply the Mamba module to address the high computational complexity caused by the self-attention mechanism in Transformers, while still maintaining good accuracy. Moreover, our system analyzes multi-scale image features and perform multi-stage refinement to reduce reconstruction errors. In the experimental results, our method showed an average vertex position error that is 1.3mm higher than a baseline method, but used only 86.5% of the parameters and had just 17.3% of the computational complexity. This demonstrates that our approach is more suitable for environments with limited computational resources, such as embedded systems.

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