INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network
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Abstract (arXiv)
We present a software that predicts non-cleft facial images for patients with cleft lip, thereby facilitating the understanding, awareness and discussion of cleft lip surgeries. To protect patients privacy, we design a software framework using image inpainting, which does not require cleft lip images for training, thereby mitigating the risk of model leakage. We implement a novel multi-task architecture that predicts both the non-cleft facial image and facial landmarks, resulting in better performance as evaluated by surgeons. The software is implemented with PyTorch and is usable with consumer-level color images with a fast prediction speed, enabling effective deployment.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 25 references that are in the map. First posted on arXiv 17 May 2023.
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13 of 25 references.
References · 13
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- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
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- 2017How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)cited by 525
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- 2021Image Inpainting via Conditional Texture and Structure Dual Generationcited by 66
- 2019LaFIn: Generative Landmark Guided Face Inpaintingcited by 12
- 2022Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networkscited by 6
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