2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)最新文献_第8页

Facial Geometric Detail Recovery via Implicit Representation 基于隐式表示的面部几何细节恢复

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-03-18 DOI: 10.1109/FG57933.2023.10042505

Xingyu Ren, Alexandros Lattas, Baris Gecer, Jiankang Deng, Chao Ma, Xiaokang Yang, S. Zafeiriou

{"title":"Facial Geometric Detail Recovery via Implicit Representation","authors":"Xingyu Ren, Alexandros Lattas, Baris Gecer, Jiankang Deng, Chao Ma, Xiaokang Yang, S. Zafeiriou","doi":"10.1109/FG57933.2023.10042505","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042505","url":null,"abstract":"Learning a dense 3D model with fine-scale details from a single facial image is highly challenging and ill-posed. To address this problem, many approaches fit smooth geometries through facial prior while learning details as additional displacement maps or personalized basis. However, these techniques typically require vast datasets of paired multi-view data or 3D scans, whereas such datasets are scarce and expensive. To alleviate heavy data dependency, we present a robust texture-guided geometric detail recovery approach using only a single in-the-wild facial image. Specifically, we inpaint occluded facial parts, generate complete textures, and build an accurate multi-view dataset of the target subject. In order to estimate the detailed geometry, we define an implicit signed distance function and employ a physically-based implicit renderer to reconstruct fine geometric details from the generated multiview images. Our method not only recovers accurate facial details but also decomposes the diffuse and specular albedo, normals and shading components in a self-supervised way. Finally, we register the implicit shape details to a 3D Morphable Model template, which can be used in traditional modeling and rendering pipelines. Extensive experiments demonstrate that the proposed approach can reconstruct impressive facial details from a single image, especially when compared with state-of-the-art methods trained on large datasets.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128244430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

S2F2: Self-Supervised High Fidelity Face Reconstruction from Monocular Image [2]基于单目图像的自监督高保真人脸重建

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-03-15 DOI: 10.1109/FG57933.2023.10042713

Abdallah Dib, Junghyun Ahn, C. Thébault, P. Gosselin, Louis Chevallier

引用次数: 4

AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit AFFDEX 2.0:一个实时面部表情分析工具包

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2022-02-24 DOI: 10.1109/FG57933.2023.10042673

M. Bishay, Kenneth Preston, Matthew Strafuss, Graham Page, Jay Turcot, Mohammad Mavadati

引用次数: 5

Segmentation-Reconstruction-Guided Facial Image De-occlusion 分割-重建引导的人脸图像去遮挡

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2021-12-15 DOI: 10.1109/FG57933.2023.10042570

Xiangnan Yin, Di Huang, Zehua Fu, Yunhong Wang, Liming Luke Chen

引用次数: 3

FLAME-in-NeRF: Neural control of Radiance Fields for Free View Face Animation 火焰-in- nerf:自由视图面部动画的辐射场的神经控制

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2021-08-10 DOI: 10.1109/FG57933.2023.10042553

ShahRukh Athar, Zhixin Shu, D. Samaras

{"title":"FLAME-in-NeRF: Neural control of Radiance Fields for Free View Face Animation","authors":"ShahRukh Athar, Zhixin Shu, D. Samaras","doi":"10.1109/FG57933.2023.10042553","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042553","url":null,"abstract":"This paper presents a neural rendering method for controllable portrait video synthesis. Recent advances in volumetric neural rendering, such as neural radiance fields (NeRF), have enabled the photorealistic novel view synthesis of static scenes with impressive results. However, modeling dynamic and controllable objects as part of a scene with such scene representations is still challenging. In this work, we design a system that enables 1) novel view synthesis for portrait video, of both the human subject and the scene they are in and 2) explicit control of the facial expressions through a low-dimensional expression representation. We represent the distribution of human facial expressions using the expression parameters of a 3D Morphable Model (3DMM) and condition the NeRF volumetric function on them. In order to guide the network to learn disentangled control for static scene appearance and dynamic facial actions, we impose a spatial prior via 3DMM fitting. We show the effectiveness of our method on free view synthesis of portrait videos with expression controls. To train a scene, our method only requires a short video of a subject captured by a mobile device.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116375125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction 用于三维人脸重建的弱监督照片真实感纹理生成

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2021-06-14 DOI: 10.1109/FG57933.2023.10042545

Xiangnan Yin, Di Huang, Zehua Fu, Yunhong Wang, Liming Chen

{"title":"Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction","authors":"Xiangnan Yin, Di Huang, Zehua Fu, Yunhong Wang, Liming Chen","doi":"10.1109/FG57933.2023.10042545","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042545","url":null,"abstract":"Although much progress has been made recently in 3D face reconstruction, most previous work has been devoted to predicting accurate and fine-grained 3D shapes. In contrast, relatively little work has focused on generating high-fidelity face textures. Compared with the prosperity of photo-realistic 2D face image generation, high-fidelity 3D face texture generation has yet to be studied. In this paper, we propose a novel UV map generation model that predicts the UV map from a single face image. The model consists of a UV sampler and a UV generator. By selectively sampling the input face image's pixels and adjusting their relative locations, the UV sampler generates an incomplete UV map that could faithfully reconstruct the original face. Missing textures in the incomplete UV map are further full-filled by the UV generator. The training is based on pseudo ground truth blended by the 3DMM texture and the input face texture, thus weakly supervised. To deal with the artifacts in the imperfect pseudo UV map, multiple UV map and face image discriminators are leveraged.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114581114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Unified Detection of Digital and Physical Face Attacks 数字和物理面部攻击的统一检测

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2021-04-05 DOI: 10.1109/FG57933.2023.10042500

Debayan Deb, Xiaoming Liu, Anil K. Jain

{"title":"Unified Detection of Digital and Physical Face Attacks","authors":"Debayan Deb, Xiaoming Liu, Anil K. Jain","doi":"10.1109/FG57933.2023.10042500","DOIUrl":"https://doi.org/10.1109/FG57933.2023.10042500","url":null,"abstract":"State-of-the-art defense mechanisms against face attacks achieve near perfect accuracies within one of three attack categories, namely adversarial, digital manipulation, or physical spoofs, however, they fail to generalize well when tested across all three categories. Poor generalization can be attributed to learning incoherent attacks jointly. To over-come this shortcoming, we propose a unified attack detection framework, namely UniFAD, that can automatically cluster 25 coherent attack types belonging to the three categories. Using a multi-task learning framework along with k-means clustering, UniFAD learns joint representations for coherent attacks, while uncorrelated attack types are learned separately. Proposed UniFAD outperforms prevailing defense methods and their fusion with an overall TDR = 94.73% @ 0.2% FDR on a large fake face dataset consisting of 341K bona fide images and 448K attack images of 25 types across all 3 categories. Proposed method can detect an attack within 3 milliseconds on a Nvidia 2080Ti. UniFAD can also identify the attack categories with 97.37% accuracy. Code and dataset will be publicly available.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129548321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

FaceGuard: A Self-Supervised Defense Against Adversarial Face Images FaceGuard:对敌对面部图像的自我监督防御

2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) Pub Date : 2020-11-28 DOI: 10.1109/FG57933.2023.10042617

Debayan Deb, Xiaoming Liu, Anil K. Jain

引用次数: 19