Superimposition-guided Facial Reconstruction from Skull
원문 초록
We develop a new algorithm to perform facial reconstruction from a given skull. This technique has forensic application in helping the identification of skeletal remains when other information is unavailable. Unlike most existing strategies that directly reconstruct the face from the skull, we utilize a database of portrait photos to create many face candidates, then perform a superimposition to get a well matched face, and then revise it according to the superimposition. To support this pipeline, we build an effective autoencoder for image-based facial reconstruction, and a generative model for constrained face inpainting. Our experiments have demonstrated that the proposed pipeline is stable and accurate.
함께 살펴볼 논문
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Face Mask Removal with Region-attentive Face Inpainting
E2F-Net: Eyes-to-Face Inpainting via StyleGAN Latent Space
Face Swap via Diffusion Model
Compensation Sampling for Improved Convergence in Diffusion Models
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting
토론
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