Publication Frequency
Quarterly (since 2025)
Journal Articles
Search
Search scope
Journal Center
Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.
Volume Arrangement
2025
2024
Featured Articles
Reconstructing the human body from monocular video input presents significant challenges, including a limited field of view and difficulty in capturing non-rigid deformations, such as those associated with clothing and pose variations. These challenges often compromise motion editability and rendering quality. To address these issues, we propose a cloth-aware 3D Gaussian splatting approach that leverages the strengths of 2D convolutional neural networks (CNNs) and 3D Gaussian splatting for high-quality human body reconstruction from monocular video. Our method parameterizes 3D Gaussians anchored to a human template to generate posed position maps that capture pose-dependent non-rigid deformations. Additionally, we introduce Learnable Cloth Features, which are pixel-aligned with the posed position maps to address cloth-related deformations. By jointly modeling cloth and pose-dependent deformations, along with compact, optimizable linear blend skinning (LBS) weights, our approach significantly enhances the quality of monocular 3D human reconstructions. We also incorporate carefully designed regularization techniques for the Gaussians, improving the generalization capability of our model. Experimental results demonstrate that our method outperforms state-of-the-art techniques for animatable avatar reconstruction from monocular inputs, delivering superior performance in both reconstruction fidelity and rendering quality.
The Charta of Rigas Velestinlis is one of the most important works of the eighteenth-century Neo-Hellenic Enlightenment and the most characteristic sample of Greek scholar cartography. Printed in Vienna in 1220 copies in 1796-1797, this emblematic map of the Balkan peninsula significantly influenced the development of ideas and perspectives that inspired the Greek War of Independence from the Ottoman Empire in 1821. The sixty (60) known remaining copies of this valuable material in Greece and abroad remain stored in the confined spaces of libraries, museums, and archives, strictly guarded for security, conservation, and preservation. This renders their access difficult to both the general public and the educational community. Since the Onassis Library and the General State Archives of Greece—Cartographic Heritage Archives both possess an original copy of this historical document each, they design and implement many educational programs aiming at highlighting its importance and reintroducing it to the public. This paper will present how the usage of new technologies, both in software and hardware, has facilitated the showcasing of cultural heritage artifacts, such as Rigas’ Charta, with an emphasis on technologies and resources that are freely available to everyone. It will also be demonstrated how the digitization projects, the digital libraries, repositories, and platforms implemented by many cultural and research organizations during the last decade, presented the opportunity for the new generation to come in contact with a variety of “locked away” historical documents, like Rigas’ Charta, allowing their reuse and reinterpretation while providing unlimited potential for the collection, research and presentation of facts, evidence and data. Furthermore, the incorporation of this digital cultural wealth in the school curriculum through targeted educational programs and the creative combination with open-source metaverse development tools, unleashed the possibilities of reviving the past, extending the life span of old materials to perpetuity. As a result, this multimodal approach paved the way for the emergence of a new more democratic, open-access, and inclusive educational model.
In this groundbreaking study, we present a novel approach to interspecies communication, focusing on the understanding of chicken vocalizations. Leveraging advanced mathematical models in artificial intelligence (AI) and machine learning, we have developed a system capable of interpreting various emotional states in chickens, including hunger, fear, anger, contentment, excitement, and distress. Our methodology employs a cutting-edge AI technique we call Deep Emotional Analysis Learning (DEAL), a highly mathematical and innovative approach that allows for the nuanced understanding of emotional states through auditory data. DEAL is rooted in complex mathematical algorithms, enabling the system to learn and adapt to new vocal patterns over time. We conducted our study with a sample of 80 chickens, meticulously recording and analyzing their vocalizations under various conditions. To ensure the accuracy of our system’s interpretations, we collaborated with a team of eight animal psychologists and veterinary surgeons, who provided expert insights into the emotional states of the chickens. Our system demonstrated an impressive accuracy rate of close to 80%, marking a significant advancement in the field of animal communication. This research not only opens up new avenues for understanding and improving animal welfare but also sets a precedent for further studies in AI-driven interspecies communication. The novelty of our approach lies in its application of sophisticated AI techniques to a largely unexplored area of study. By bridging the gap between human and animal communication, we believe our research will pave the way for more empathetic and effective interactions with the animal kingdom.
Automatic labeling of 3D facial acupoint landmarks
Article ID: 2476
Vol 5, Issue 1, 2024
DOI: https://doi.org/10.54517/m.v5i1.2476
Vol 5, Issue 1, 2024
Received: 8 January, 2024; Accepted: 6 March, 2024; Available online: 19 March, 2024; Issue release: 30 June, 2024
Download PDF
Abstract
As special marks on a human face, facial landmarks reflect the facial features of various parts of the face, which is crucial in biomedicine and medical imaging. In addition, facial landmarks are also important features in computer vision such as face detection, face recognition, facial pose estimation, and facial animation. In this paper, we construct a 3D facial acupoint annotated dataset by labeling 37 facial acupoints on 846 neutral face triangle mesh on the FaceScape dataset. Based on these annotated data, we use a feature template matching method to realize the automatic annotation of 37 acupoints on triangle meshes. We used 40 meshes as the training set to extract the geometric patterns of 3D acupoints and then measured the performance of the automatic labeling algorithm on 20 meshes and 806 meshes as the test sets. In the training process, we extract the tangent plane for each landmark, project the neighbor vertices of the landmark to the tangent plane, and construct the feature image with R × R resolution through the bounding box of the projected points. In the testing process, we use the pattern images extracted during training to find the average features and use them as a guide to optimize the predicted landmarks. The experimental results show that our automatic acupoint labeling method has achieved good results.
Keywords
landmark detection; local geometric feature; acupoints; dataset
References
- Vrochidou E, Papić V, Kalampokas T, Papakostas GA. Automatic Facial Palsy Detection—From Mathematical Modeling to Deep Learning. Axioms. 2023; 12(12): 1091. doi: 10.3390/axioms12121091
- Kim S, An H, Song M, Lee S, et al. Automated Marker-Less Patient-to-Preoperative Medical Image Registration Approach Using RGB-D Images and Facial Landmarks for Potential Use in Computed-Aided Surgical Navigation of the Paranasal Sinus. In: Advances in Computer Graphics. Cham: Springer Nature Switzerland; 2024. pp. 135–145.
- Mercan E, Atmosukarto I, Wu J, et al. Craniofacial Image Analysis. In: Health Monitoring and Personalized Feedback using Multimedia Data. Cham: Springer International Publishing; 2015. pp. 9–29.
- Liu F, Zhao Q, Liu X, Zeng D. Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2020; 42(3): 664-678. doi: 10.1109/tpami.2018.2885995
- Sharma U, Faisal KN, Sharma RR, Arya KV. Facial Landmark-Based Human Emotion Recognition Technique for Oriented Viewpoints in the Presence of Facial Attributes. SN Computer Science. 2023; 4(3). doi: 10.1007/s42979-023-01727-y
- Gupta A, Thakkar K, Gandhi V, Narayanan PJ. Nose, eyes and ears: Head pose estimation by locating facial keypoints. arXiv. 2018.
- Cootes TF, Taylor CJ, Cooper DH, Graham J. Active Shape Models-Their Training and Application. Computer Vision and Image Understanding. 1995; 61(1): 38-59. doi: 10.1006/cviu.1995.1004
- Cootes TF, Edwards GJ, Taylor CJ. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001; 23(6): 681-685. doi: 10.1109/34.927467
- Yang H, Zhu H, Wang Y, et al. FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Published online June 2020. doi: 10.1109/cvpr42600.2020.00068
- Farkas LG. Anthropometry of the head and face, 2nd ed. Available online: https://api.semanticscholar.org/CorpusID:57441646 (accessed on 23 March 2023).
- Qiao H, Tan J, Wen S, et al. De Novo Dissecting the Three-Dimensional Facial Morphology of 2379 Han Chinese Individuals. Phenomics. Published online June 8, 2023. doi: 10.1007/s43657-023-00109-x
- Liang Y, Liu H, Gao Z, et al. Ocular phenotype related SNP analysis in Southern Han Chinese population from Guangdong province. Gene. 2022; 826: 146458. doi: 10.1016/j.gene.2022.146458
- Ercan I, Ozdemir ST, Etoz A, et al. Facial asymmetry in young healthy subjects evaluated by statistical shape analysis. Journal of Anatomy. 2008; 213(6): 663-669. doi: 10.1111/j.1469-7580.2008.01002.x
- Sforza C, Rosati R, De Menezes M, et al. Three-Dimensional Computerized Anthropometry of the Nose. In: Handbook of Anthropometry. New York, NY: Springer New York; 2012. pp. 927–942.
- Agbolade O, Nazri A, Yaakob R, et al. Morphometric approach to 3D soft-tissue craniofacial analysis and classification of ethnicity, sex, and age. Wilkinson C, ed. PLOS ONE. 2020; 15(4): e0228402. doi: 10.1371/journal.pone.0228402
- Deutsch CK, Shell AR, Francis RW, Bird BD. The Farkas System of Craniofacial Anthropometry: Methodology and Normative Databases. In: Handbook of Anthropometry. New York, NY: Springer New York; 2012. pp. 561–573.
- Zhuang W, Chen L, Hong C, et al. FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition. Electronics. 2019; 8(7): 807. doi: 10.3390/electronics8070807
- Wang X, Wang Y, Li W, et al. Facial Expression Animation by Landmark Guided Residual Module. IEEE Transactions on Affective Computing. 2023; 14(2): 878-894. doi: 10.1109/taffc.2021.3100352
- Otberdout N, Daoudi M, Kacem A, et al. Dynamic Facial Expression Generation on Hilbert Hypersphere With Conditional Wasserstein Generative Adversarial Nets. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022; 44(2): 848-863. doi: 10.1109/tpami.2020.3002500
- Kostinger M, Wohlhart P, Roth PM, Bischof H. Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain, Nov. 2011, pp. 2144–2151. doi: 10.1109/ICCVW.2011.6130513
- Sagonas C, Antonakos E, Tzimiropoulos G, et al. 300 Faces In-The-Wild Challenge: database and results. Image and Vision Computing. 2016; 47: 3-18. doi: 10.1016/j.imavis.2016.01.002
- Zhu X,Ramanan D. Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, Jun. 2012, pp. 2879–2886. doi: 10.1109/CVPR.2012.6248014
- Le V, Brandt J, Lin Z, et al. Interactive Facial Feature Localization. In Computer Vision—ECCV 2012. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012. pp. 679–692. doi: 10.1007/978-3-642-33712-3_49
- Deng J, Roussos A, Chrysos G, et al. The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking. International Journal of Computer Vision. 2018; 127(6-7): 599-624. doi: 10.1007/s11263-018-1134-y
- Zhu X, Lei Z, Liu X, et al. Face Alignment Across Large Poses: A 3D Solution. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Published online June 2016. doi: 10.1109/cvpr.2016.23
- Wu Y, Ji Q. Facial Landmark Detection: A Literature Survey. International Journal of Computer Vision. 2018; 127(2): 115-142. doi: 10.1007/s11263-018-1097-z
- Baker S, Matthews I. Lucas-Kanade 20 Years On: A Unifying Framework. Int. J. Comput. Vis. 2004; 56(3): 221–255. doi: 10.1023/B:VISI.0000011205.11775.fd
- Matthews I, Baker S. Active Appearance Models Revisited. Int. J. Comput. Vis. 2004; 60(2): 135–164. doi: 10.1023/B:VISI.0000029666.37597.d3
- Gross R, Matthews I, Baker S. Appearance-based face recognition and light-fields. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2004; 26(4): 449-465. doi: 10.1109/tpami.2004.1265861
- Gross R, Matthews I, Baker S. Generic vs. person specific active appearance models. Image and Vision Computing. 2005; 23(12): 1080-1093. doi: 10.1016/j.imavis.2005.07.009
- Cristinacce D, Cootes TF. Feature Detection and Tracking with Constrained Local Models. In: Proceedings of the British Machine Vision Conference 2006, Edinburgh, 2006, pp. 95.1-95.10. doi: 10.5244/C.20.95
- Dantone M, Gall J, Fanelli G, Van Gool L. Real-time facial feature detection using conditional regression forests. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, Jun. 2012, pp. 2578–2585. doi: 10.1109/CVPR.2012.6247976
- Zhang J, Hu H, Feng S. Robust Facial Landmark Detection via Heatmap-Offset Regression. IEEE Transactions on Image Processing. 2020; 29: 5050-5064. doi: 10.1109/tip.2020.2976765
- Jeong M, Ko BC, Kwak S, Nam JY. Driver Facial Landmark Detection in Real Driving Situations. IEEE Transactions on Circuits and Systems for Video Technology. 2018; 28(10): 2753-2767. doi: 10.1109/tcsvt.2017.2769096
- Deng Z, Liu H. Geometry-attentive relational reasoning for robust facial landmark detection. Neurocomputing. 2021; 453: 790-800. doi: 10.1016/j.neucom.2020.06.126
- Wu Y, Ji Q. Discriminative Deep Face Shape Model for Facial Point Detection. International Journal of Computer Vision. 2014; 113(1): 37-53. doi: 10.1007/s11263-014-0775-8
- Salazar A, Wuhrer S, Shu C, Prieto F. Fully automatic expression-invariant face correspondence. Machine Vision and Applications. 2013; 25(4): 859-879. doi: 10.1007/s00138-013-0579-9
- Papazov C, Marks TK, Jones M. Real-time 3D head pose and facial landmark estimation from depth images using triangular surface patch features. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, Jun. 2015, pp. 4722–4730. doi: 10.1109/CVPR.2015.7299104
- Berends B, Bielevelt F, Schreurs R, et al. Fully automated landmarking and facial segmentation on 3D photographs. arXiv. 2023.
- Perakis P, Passalis G, Theoharis T, Kakadiaris IA. 3D Facial Landmark Detection under Large Yaw and Expression Variations. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013; 35(7): 1552-1564. doi: 10.1109/tpami.2012.247
- Wang J, Fang S, Fang M, et al. Automatic Landmark Placement for Large 3D Facial Image Dataset. In: 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, Dec. 2019, pp. 5088–5093. doi: 10.1109/BigData47090.2019.9006310
- Wang Z, Simoncelli EP, Bovik AC. Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, Pacific Grove, CA, USA, 2003, pp. 1398–1402. doi: 10.1109/ACSSC.2003.1292216
- Zhou QY, Park J, Koltun V. Open3D: A Modern Library for 3D Data Processing. arXiv. 2018.
- Ingale AK, Leema AA, Kim H, Udayan JD. Automatic 3D Facial Landmark-Based Deformation Transfer on Facial Variants for Blendshape Generation. Arabian Journal for Science and Engineering. 2022; 48(8): 10109-10123. doi: 10.1007/s13369-022-07403-2
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Junjie Yin, Meie Fang, Weiyin Ma
License URL: https://creativecommons.org/licenses/by/4.0/
This site is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Academic Supporter
Editor-in-Chief
Prof. Zhigeng Pan
Director, Institute for Metaverse, Nanjing University of Information Science & Technology, China
Honorary Editor-in-Chief
Prof. Jianrong Tan
Academician, Chinese Academy of Engineering, China
Indexing & Archiving
.jpg)
.jpg)
News & Announcements
2025-05-20
Conference Time
December 15-18, 2025
Conference Venue
Hong Kong Convention and Exhibition Center (HKCEC)
...
2025-04-23
Metaverse Scientist Forum No.3 was successfully held on April 22, 2025, from 19:00 to 20:30 (Beijing Time)...
2025-04-21
We received the Scopus notification on April 19th, confirming that the journal has been successfully indexed by Scopus...
2025-04-15
We are pleased to announce that we have updated the requirements for manuscript figures in the submission guidelines. Manuscripts submitted after April 15, 2025 are required to strictly adhere to the change. These updates are aimed at ensuring the highest quality of visual content in our publications and enhancing the overall readability and impact of your research. For more details, please find it in sumissions...
