Details

Autor(en) / Beteiligte

• Liao, Zhouzhou
• Su, Zewei
• Dong, Lanfang

Titel

Animatable 3D Facial Detail Reconstruction from In-the-wild Images

Ist Teil von

• Proceedings of the 2024 7th International Conference on Image and Graphics Processing, 2024, p.244-251

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2024

Quelle

ACM Digital Library

Beschreibungen/Notizen

• With the introduction of the "metaverse" concept, digital human technology has garnered widespread attention, and how to quickly model a high-precision 3D human face has become a research focus in the field of digital humans. To address the issue of 3D Morphable Models (3DMM) being unable to reconstruct facial details, this paper proposes an animatable facial detail generation algorithm based on generative adversarial loss, which is used to restore more facial reliefs on the basic face obtained from three-dimensional facial reconstruction, achieving a highly realistic reconstruction of digital human faces. Firstly, this paper employs displacement mapping technology to recover facial detail information, constructing an autoencoder network to predict displacement maps from input images, controlling the vertices of the 3D model to express more relief details. Secondly, to effectively capture the mid-to-high frequency details of the face, generative adversarial loss is introduced to model the high-frequency attribute differences between images, jointly constrained with photometric losses and other content losses to ensure the accuracy of generated facial details. Lastly, to decouple static from dynamic facial details, this paper embeds 3DMM expression-related parameters into the generator model of displacement maps, and adopts an identity averaging strategy to constrain the encoder network to model only the static details of the face, whereas the dynamic details are provided by the 3DMM parameters, enabling the generation of dynamic facial details driven by expressions. The final experimental results show that the algorithm proposed in this paper demonstrates competitive performance in terms of model reconstruction quality and accuracy when compared to other state-of-the-art algorithms in facial detail generation. Additionally, the ability of the model to dynamically construct facial details was verified through expression transfer experiments, and the importance of generative adversarial loss was confirmed through ablation experiments.