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研究生： Rohit Das
Rohit Das
論文名稱： 3D-GANTex:基於StyleGAN3的多視角圖像和3DDFA的網格生成方式重建3D人臉
3D-GANTex: 3D Face Reconstruction with StyleGAN3-based Multi-View Images and 3DDFA based Mesh Generation
指導教授： 王科植
Wang, Ko-Chih
林宗翰
Lin, Tzung-Han
口試委員： 孫沛立
Sun, Pei-Li
葉 梅珍
Yeh, Mei-Chen
王科植
Wang, Ko-Chih
林宗翰
Lin, Tzung-Han
口試日期： 2023/06/27
學位類別： 碩士
Master
系所名稱： 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2023
畢業學年度： 111
語文別： 英文
論文頁數： 89
英文關鍵詞： 3D Face Reconstruction, Generative Adversarial Network(GAN), Latent Space, Texture Map, Multi-View Generation, StyleGAN3
研究方法: 實驗設計法 、 參與觀察法
DOI URL： http://doi.org/10.6345/NTNU202300718
論文種類： 學術論文
相關次數： 點閱：88  下載：1 
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    • Texture estimation from a single image is a challenging task due to the lack of texture information available and limited training data. This thesis proposes a novel approach for texture estimation from a single in the wild image using a Generative Adversarial Network (GAN) and 3D Dense Face Alignment (3DDFA). The method begins by generating multi-view faces using the latent space of GAN. Then 3DDFA generates a 3D face mesh as well as a high-resolution texture map that is consistent with the estimated face shape. The generated texture map is later refined using an iterative process that incorporates information from both the input image and the estimated 3D face shape.

    Studies have been conducted to investigate the contributions of different components of the mentioned method, and show that:
    1. Use of the GAN latent space can be a critical benchmark for achieving high-quality results.
    2. Editing the latent space can generate high quality multi-view images.
    3. Generating 3D mesh and texture map estimation from a single image is possible with a very high accuracy.

    To evaluate the effectiveness of this approach, experiments were conducted on in-the-wild images and the results were compared with state of-the-art 3D Scanner. To verify that, subjective valuation has been performed on 16 participants. The results prove that the mentioned method outperforms existing method in terms of performance, demonstrating the effectiveness of this approach.

    Results generated from the aforementioned method are very accurate and has the potential to serve as an important contribution in avatar creation as well as 3D Face Reconstruction.

    In summary, the proposed method for texture estimation from a single image using GAN latent space and 3DDFA represents a significant advancement in the field of computer vision and has potential applications in a wide range of fields, including virtual try-on, facial recognition, beauty industry as well as metaverse.

    1. Introduction 1 1.1 Objective 1 1.2 Face Frontalization 2 1.3 Texture Generation 3 1.4 3D Model Generation 4 1.5 Challenges for 3D Model Generation 5 1.6 Proposed Framework 6 2. Literature Review 7 2.1 Generative Adversarial Networks (GAN) 7 2.2 Latent Space 8 2.3 Multi-View Face Generation 11 2.4 Face Rotation 12 2.5 Encoder 13 2.6 Texture 16 2.6.1 Research based on 3D Morphable Model (3DMM) 17 2.6.2 3D Dense Face Alignment 3DDFA 18 2.7 Dataset 19 3. Methodology 21 3.1 Latent Space Embedding 21 3.2 Encoder 22 3.2.1 Restyle Encoder 22 3.3 Encoder4Editing(e4e) Encoder 23 3.3.1 ReStyle-e4e Encoder 25 3.4 InterFaceGAN 26 3.5 Loss Functions 28 3.5.1 Pixel-Wise Loss 29 3.5.2 LPIPS Loss 30 3.5.3 Identity Based Reconstruction 31 3.6 Generate 3D Face Model and Texture Maps using 3DDFA 32 3.6.1 Normalized Coordinate Code (NCC) 32 3.6.2 Projected Normalized Coordinate Code (PNCC) 32 3.6.3 Pose Adaptive Convolutions 34 4. Experiments and Evaluation 36 4.1 Hardware and Environment 37 4.2 Dataset- Flickr-Faces High Quality Dataset (FFHQ) 37 4.3 Embedding the Image to Latent Space 38 4.4 Multi-View Synthesis using InterFaceGAN 39 4.5 Generate Texture Map and 3D Model using 3DDFA 40 4.6 Evaluation Metric for StyleGAN3 Generated Images 41 4.6.1 Structural Similarity Index (SSIM) 41 4.6.2 Feature Similarity Index (FSIM) 42 4.6.3 Perceptual Loss 42 4.6.4 Multiscale Structural Similarity Index (MS-SSIM) 43 4.6.5 Evaluation with No Background 44 4.7 Evaluation Metric for Generated UV Map 44 4.7.1 Pixel Density 44 4.7.2 Texture Density 44 4.8 Evaluation 3D Face Mesh and Texture using Hardware 50 4.9 Subjective Evaluation 57 5. Results and Discussions 71 5.1 Generation of Multi-View Images 71 5.2 Generating UV Map 74 5.3 Creating 3D Model using 3DDFA 76 6. Conclusion 80 References 81 Appendix 88 Appendix 1 88 Appendix 2 89

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