3D Human Model Reconstruction Based on Implicit Representation

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.