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Portrait stylized rendering for 3D light-field display based on radiation field and example guide

机译:基于辐射场的3D光场显示人像风格化渲染及示例指南

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With the development of three-dimensional (3D) light-field display technology, 3D scenes with correct location information and depth information can be perceived without wearing any external device. Only 2D stylized portrait images can be generated with traditional portrait stylization methods and it is difficult to produce high-quality stylized portrait content for 3D light-field displays. 3D light-field displays require the generation of content with accurate depth and spatial information, which is not achievable with 2D images alone. New and innovative portrait stylization techniques methods should be presented to meet the requirements of 3D light-field displays. A portrait stylization method for 3D light-field displays is proposed, which maintain the consistency of dense views in light-field display when the 3D stylized portrait is generated. Example-based portrait stylization method is used to migrate the designated style image to the portrait image, which can prevent the loss of contour information in 3D light-field portraits. To minimize the diversity in color information and further constrain the contour details of portraits, the Laplacian loss function is introduced in the pre-trained deep learning model. The three-dimensional representation of the stylized portrait scene is reconstructed, and the stylized 3D light field image of the portrait is generated the mask guide based light-field coding method. Experimental results demonstrate the effectiveness of the proposed method, which can use the real portrait photos to generate high quality 3D light-field portrait content. COPY; 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
机译:随着三维(3D)光场显示技术的发展,无需佩戴任何外部设备即可感知具有正确位置信息和深度信息的3D场景。传统的人像风格化方法只能生成2D风格化的人像图像,难以产生高质量的风格化人像内容用于3D光场显示。3D 光场显示器需要生成具有准确深度和空间信息的内容,而仅使用 2D 图像无法实现这一点。应提出新的和创新的人像风格化技术,以满足3D光场显示的要求。该文提出一种用于3D光场显示的人像风格化方法,该方法在生成3D风格化人像时保持了光场显示中密集视图的一致性。采用基于示例的人像风格化方法,将指定样式的图像迁移到人像图像中,可以防止3D光场人像中轮廓信息的丢失。为了最小化颜色信息的多样性,并进一步约束人像的轮廓细节,在预训练的深度学习模型中引入了拉普拉斯损失函数。重构了风格化人像场景的三维表示,并采用基于掩模引导的光场编码方法生成了人像的程式化三维光场图像。实验结果证明了所提方法的有效性,该方法可以利用真实的人像照片生成高质量的3D光场人像内容。2023 Optica Publishing Group 根据 Optica 开放获取出版协议的条款

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