3D Facial Imperfection Regeneration: Deep learning approach and 3D printing prototypes

• URL: http://arxiv.org/abs/2303.14381v1
• Date: Sat, 25 Mar 2023 07:12:33 GMT
• Title: 3D Facial Imperfection Regeneration: Deep learning approach and 3D printing prototypes
• Authors: Phuong D. Nguyen, Thinh D. Le, Duong Q. Nguyen, Thanh Q. Nguyen, Li-Wei Chou, H. Nguyen-Xuan
• Abstract summary: This study explores the potential of a fully convolutional mesh autoencoder model for regenerating 3D nature faces with the presence of imperfect areas. We utilize deep learning approaches in graph processing and analysis to investigate the capabilities model in recreating a filling part for facial scars.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study explores the potential of a fully convolutional mesh autoencoder model for regenerating 3D nature faces with the presence of imperfect areas. We utilize deep learning approaches in graph processing and analysis to investigate the capabilities model in recreating a filling part for facial scars. Our approach in dataset creation is able to generate a facial scar rationally in a virtual space that corresponds to the unique circumstances. Especially, we propose a new method which is named 3D Facial Imperfection Regeneration(3D-FaIR) for reproducing a complete face reconstruction based on the remaining features of the patient face. To further enhance the applicable capacity of the present research, we develop an improved outlier technique to separate the wounds of patients and provide appropriate wound cover models. Also, a Cir3D-FaIR dataset of imperfect faces and open codes was released at https://github.com/SIMOGroup/3DFaIR. Our findings demonstrate the potential of the proposed approach to help patients recover more quickly and safely through convenient techniques. We hope that this research can contribute to the development of new products and innovative solutions for facial scar regeneration.

Related papers

• FitDiff: Robust monocular 3D facial shape and reflectance estimation using Diffusion Models [79.65289816077629]
  We present FitDiff, a diffusion-based 3D facial avatar generative model. Our model accurately generates relightable facial avatars, utilizing an identity embedding extracted from an "in-the-wild" 2D facial image. Being the first 3D LDM conditioned on face recognition embeddings, FitDiff reconstructs relightable human avatars, that can be used as-is in common rendering engines.
  arXiv  Detail & Related papers  (2023-12-07T17:35:49Z)
• Advancing Wound Filling Extraction on 3D Faces: Auto-Segmentation and Wound Face Regeneration Approach [0.0]
  We propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network. Based on the segmentation model, we propose an improved approach for extracting 3D facial wound fillers. Our method achieved a remarkable accuracy of 0.9999986% on the test suite, surpassing the performance of the previous method.
  arXiv  Detail & Related papers  (2023-07-04T17:46:02Z)
• Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency [5.040145546652933]
  We propose a novel method for anonymised face generation for 3D T1-weighted scans based on a 3D conditional generative adversarial network. The proposed method takes 9 seconds for face generation and is suitable for recovering consistent post-processing results after defacing.
  arXiv  Detail & Related papers  (2023-05-26T13:34:14Z)
• Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures [0.05999777817331315]
  We present an innovative method for regenerating flawed facial structures, yielding 3D printable outputs. Our results highlight the model's capacity for concealing scars and achieving comprehensive facial reconstructions without discernible scarring.
  arXiv  Detail & Related papers  (2023-04-08T16:13:30Z)
• High-fidelity Facial Avatar Reconstruction from Monocular Video with Generative Priors [29.293166730794606]
  We propose a new method for NeRF-based facial avatar reconstruction that utilizes 3D-aware generative prior. Compared with existing works, we obtain superior novel view synthesis results and faithfully face reenactment performance.
  arXiv  Detail & Related papers  (2022-11-28T04:49:46Z)
• 3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling [111.98096975078158]
  We introduce a style-based generative network that synthesizes in one pass all and only the required rendering samples of a neural radiance field. We show that this model can accurately be fit to "in-the-wild" facial images of arbitrary pose and illumination, extract the facial characteristics, and be used to re-render the face in controllable conditions.
  arXiv  Detail & Related papers  (2022-09-15T15:28:45Z)
• AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs [119.23922747230193]
  We introduce the first method that is able to reconstruct render-ready 3D facial geometry and BRDF from a single "in-the-wild" image. Our method outperforms the existing arts by a significant margin and reconstructs high-resolution 3D faces from a single low-resolution image.
  arXiv  Detail & Related papers  (2021-12-11T11:36:30Z)
• Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction [76.1612334630256]
  We harness the power of Generative Adversarial Networks (GANs) and Deep Convolutional Neural Networks (DCNNs) to reconstruct the facial texture and shape from single images. We demonstrate excellent results in photorealistic and identity preserving 3D face reconstructions and achieve for the first time, facial texture reconstruction with high-frequency details.
  arXiv  Detail & Related papers  (2021-05-16T16:35:44Z)
• Inverting Generative Adversarial Renderer for Face Reconstruction [58.45125455811038]
  In this work, we introduce a novel Generative Adversa Renderer (GAR) GAR learns to model the complicated real-world image, instead of relying on the graphics rules, it is capable of producing realistic images. Our method achieves state-of-the-art performances on multiple face reconstruction.
  arXiv  Detail & Related papers  (2021-05-06T04:16:06Z)
• Learning Complete 3D Morphable Face Models from Images and Videos [88.34033810328201]
  We present the first approach to learn complete 3D models of face identity geometry, albedo and expression just from images and videos. We show that our learned models better generalize and lead to higher quality image-based reconstructions than existing approaches.
  arXiv  Detail & Related papers  (2020-10-04T20:51:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.