Reconstructing mesh guided high definition facial model from monocular video

Chenglai Zhong; Haiyao Xiao; Yudong Guo

doi:10.1117/12.3031587

18 July 2024 Reconstructing mesh guided high definition facial model from monocular video

Chenglai Zhong, Haiyao Xiao, Yudong Guo

Author Affiliations +

Proceedings Volume 13179, International Conference on Optics and Machine Vision (ICOMV 2024); 131791G (2024) https://doi.org/10.1117/12.3031587
Event: International Conference on Optics and Machine Vision (ICOMV 2024), 2024, Nanchang, China

Abstract

In the past year or two, various implicit representations have made significant breakthroughs in reconstructing heads, faces, and objects. For example, an implicit digital avatar can be created from a video. But there is an obvious problem with such methods. They have big issues with the compatibility and editability of existing methods. The method we proposed can effectively solve this problem and obtain a high-fidelity explicit geometric head avatar through the same data output. At the same time, because it has a specific geometry, it can be changed by changing the position of the vertices and the texture. Color to facilitate editing of the current model. At the same time, because of the properties of explicit geometry, it can also be easily applied to various rendering engines and frameworks. At the same time, if you only use explicit geometry methods, there will naturally be problems with expression ability. Therefore, our method is to combine explicit and implicit, using probability theory and other related knowledge to build this model so that it can have both the advantages of the person. Our method does perform well on the shortcomings of both.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chenglai Zhong, Haiyao Xiao, and Yudong Guo "Reconstructing mesh guided high definition facial model from monocular video", Proc. SPIE 13179, International Conference on Optics and Machine Vision (ICOMV 2024), 131791G (18 July 2024); https://doi.org/10.1117/12.3031587

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