Face repairing based on transfer learning method with fewer training samples: application to a Terracotta Warrior with facial cracks and a Buddha with a broken nose

Face repairing based on transfer learning method with fewer training samples: application to a Terracotta Warrior with facial cracks and a Buddha with a broken nose

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Abstract In this paper, a method based on transfer learning is proposed to recover the three-dimensional shape of cultural relics faces from a single old photo.It can simultaneously reconstruct the three-dimensional facial structure and align the texture of the cultural relics with fewer training samples.The UV position map is read more used to represent the three-dimensional shape in space and act as the output of the network.A convolutional neural network is used to reconstruct the UV position map from a single 2D image.In the training process, the human face data is used for pre-training, and then a small amount of artifact data is used for fine-tuning.

A deep learning model with el reformador tequila anejo strong generalization ability is trained with fewer artifact data, and a three-dimensional model of the cultural relic face can be reconstructed from a single old photograph.The methods can train more complex deep networks without a large amount of cultural relic data, and no over-fitting phenomenon occurs, which effectively solves the problem of fewer cultural relic samples.The method is verified by restoring a Chinese Terracotta Warrior with facial cracks and a Buddha with a broken nose.Other applications can be used in the fields such as texture recovery, facial feature extraction, and three-dimensional model estimation of the damaged cultural relics or sculptures in the photos.