@inproceedings{05281fdade704c13b6bdbe5f32e1f86f,
title = "3D Human Model Reconstruction Based on Implicit Representation",
abstract = "With the rapid development of artificial intelligence in recent years, various industries have been replacing or aiding manpower through machines to reduce production costs. In order to make the naturalness of the characters closer to the real life, game developers need to develop 3D human models together with animation designers, but the time and money spent are too high, which increases the development cost. Therefore, using deep learning to develop 3D human models without the assistance of scanning instruments can significantly reduce game development costs. In the research, the 3D human model is reconstructed from a single image and trained with deep learning to achieve a high-quality reconstruction with a small dataset. Recent literature has trained with a large amount of data, which not only takes a lot of time and increases the cost of purchasing training materials but is also not available for personal use. In order to train the model with a small number of datasets, this study adapted the network architecture to accommodate low database training, which can ensure the use of the 3D human model by individuals in non-corporate enterprises. The addition of Attention to the model allows it to extract important features during training, improving the quality of the reconstructed 3D human model and reducing the time it takes to update parameters. In addition, the reconstructed model has not only geometry but also color representation, which can be used in a wider range of applications. Both have outstanding performance in objective evaluation or evaluation of reconstructed 3D human model.",
keywords = "Attention Mechanism, Deep Learning, Reconstruction of 3D Human Body Model",
author = "Liang, {Kai Wen} and Guo, {You Sheng} and Wang, {Chien Yao} and Le, {Phuong Thi} and Putri, {Wenny Ramadha} and Chen, {Yung Fang} and Pao-Chi Chang and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 24th International Conference on Computational Science and Its Applications, ICCSA 2024 ; Conference date: 01-07-2024 Through 04-07-2024",
year = "2024",
doi = "10.1007/978-3-031-65343-8_30",
language = "???core.languages.en_GB???",
isbn = "9783031653421",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "386--397",
editor = "Osvaldo Gervasi and Beniamino Murgante and Chiara Garau and David Taniar and {C. Rocha}, {Ana Maria A.} and {Faginas Lago}, {Maria Noelia}",
booktitle = "Computational Science and Its Applications – ICCSA 2024 Workshops, Proceedings",
}