PG2022 Short Papers, Posters, and Work-in-Progress Papers
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Browsing PG2022 Short Papers, Posters, and Work-in-Progress Papers by Subject "Computing methodologies → Mesh models"
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Item Human Face Modeling based on Deep Learning through Line-drawing(The Eurographics Association, 2022) Kawanaka, Yuta; Sato, Syuhei; Sakurai, Kaisei; Gao, Shangce; Tang, Zheng; Yang, Yin; Parakkat, Amal D.; Deng, Bailin; Noh, Seung-TakThis paper presents a deep learning-based method for creating 3D human face models. In recent years, several sketch-based shape modeling methods have been proposed. These methods allow the user to easily model various shapes containing animal, building, vehicle, and so on. However, a few methods have been proposed for human face models. If we can create 3D human face models via line-drawing, models of cartoon or fantasy characters can be easily created. To achieve this, we propose a sketch-based face modeling method. When a single line-drawing image is input to our system, a corresponding 3D face model are generated. Our system is based on a deep learning; many human face models and corresponding images rendered as line-drawing are prepared, and then a network is trained using these datasets. For the network, we use a previous method for reconstructing human bodies from real images, and we propose some extensions to enhance learning accuracy. Several examples are shown to demonstrate usefulness of our system.