AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
How to
Here you can find the complete list of our publications.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Li, C.; Da, F.
Refined dense face alignment through image matching Journal Article
In: Visual Computer, vol. 41, no. 1, pp. 157–171, 2025, ISSN: 01782789 (ISSN).
Abstract | Links | BibTeX | Tags: 3D Avatars, Alignment, Dense geometric supervision, Face alignment, Face deformations, Face reconstruction, Geometry, Human computer interaction, Image enhancement, Image matching, Image Reconstruction, Metaverses, Outlier mixup, Pixels, Rendered images, Rendering (computer graphics), State of the art, Statistics, Target images, Three dimensional computer graphics
@article{li_refined_2025,
title = {Refined dense face alignment through image matching},
author = {C. Li and F. Da},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187924785&doi=10.1007%2fs00371-024-03316-3&partnerID=40&md5=839834c6ff3320398d5ef75b055947cb},
doi = {10.1007/s00371-024-03316-3},
issn = {01782789 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Visual Computer},
volume = {41},
number = {1},
pages = {157–171},
abstract = {Face alignment is the foundation of building 3D avatars for virtue communication in the metaverse, human-computer interaction, AI-generated content, etc., and therefore, it is critical that face deformation is reflected precisely to better convey expression, pose and identity. However, misalignment exists in the currently best methods that fit a face model to a target image and can be easily captured by human perception, thus degrading the reconstruction quality. The main reason is that the widely used metrics for training, including the landmark re-projection loss, pixel-wise loss and perception-level loss, are insufficient to address the misalignment and suffer from ambiguity and local minimums. To address misalignment, we propose an image MAtchinG-driveN dEnse geomeTrIC supervision (MAGNETIC). Specifically, we treat face alignment as a matching problem and establish pixel-wise correspondences between the target and rendered images. Then reconstructed facial points are guided towards their corresponding points on the target image, thus improving reconstruction. Synthesized image pairs are mixed up with face outliers to simulate the target and rendered images with ground-truth pixel-wise correspondences to enable the training of a robust prediction network. Compared with existing methods that turn to 3D scans for dense geometric supervision, our method reaches comparable shape reconstruction results with much lower effort. Experimental results on the NoW testset show that we reach the state-of-the-art among all self-supervised methods and even outperform methods using photo-realistic images. We also achieve comparable results with the state-of-the-art on the benchmark of Feng et al. Codes will be available at: github.com/ChunLLee/ReconstructionFromMatching. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.},
keywords = {3D Avatars, Alignment, Dense geometric supervision, Face alignment, Face deformations, Face reconstruction, Geometry, Human computer interaction, Image enhancement, Image matching, Image Reconstruction, Metaverses, Outlier mixup, Pixels, Rendered images, Rendering (computer graphics), State of the art, Statistics, Target images, Three dimensional computer graphics},
pubstate = {published},
tppubtype = {article}
}
Face alignment is the foundation of building 3D avatars for virtue communication in the metaverse, human-computer interaction, AI-generated content, etc., and therefore, it is critical that face deformation is reflected precisely to better convey expression, pose and identity. However, misalignment exists in the currently best methods that fit a face model to a target image and can be easily captured by human perception, thus degrading the reconstruction quality. The main reason is that the widely used metrics for training, including the landmark re-projection loss, pixel-wise loss and perception-level loss, are insufficient to address the misalignment and suffer from ambiguity and local minimums. To address misalignment, we propose an image MAtchinG-driveN dEnse geomeTrIC supervision (MAGNETIC). Specifically, we treat face alignment as a matching problem and establish pixel-wise correspondences between the target and rendered images. Then reconstructed facial points are guided towards their corresponding points on the target image, thus improving reconstruction. Synthesized image pairs are mixed up with face outliers to simulate the target and rendered images with ground-truth pixel-wise correspondences to enable the training of a robust prediction network. Compared with existing methods that turn to 3D scans for dense geometric supervision, our method reaches comparable shape reconstruction results with much lower effort. Experimental results on the NoW testset show that we reach the state-of-the-art among all self-supervised methods and even outperform methods using photo-realistic images. We also achieve comparable results with the state-of-the-art on the benchmark of Feng et al. Codes will be available at: github.com/ChunLLee/ReconstructionFromMatching. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
2024
Martini, M.; Valentini, V.; Ciprian, A.; Bottino, A.; Iacoviello, R.; Montagnuolo, M.; Messina, A.; Strada, F.; Zappia, D.
Semi -Automated Digital Human Production for Enhanced Media Broadcasting Proceedings Article
In: IEEE Gaming, Entertain., Media Conf., GEM, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835037453-7 (ISBN).
Abstract | Links | BibTeX | Tags: AI automation, Automation, Creation process, Digital humans, Economic and social effects, Extensive explorations, Face reconstruction, Generative AI, Image enhancement, media archive, Media archives, Metaverses, Rendering (computer graphics), Synthetic human, Synthetic Humans, Textures, Three dimensional computer graphics, Virtual production, Virtual Reality
@inproceedings{martini_semi_2024,
title = {Semi -Automated Digital Human Production for Enhanced Media Broadcasting},
author = {M. Martini and V. Valentini and A. Ciprian and A. Bottino and R. Iacoviello and M. Montagnuolo and A. Messina and F. Strada and D. Zappia},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199536742&doi=10.1109%2fGEM61861.2024.10585601&partnerID=40&md5=3703fba931b02f9615316db8ebbca70c},
doi = {10.1109/GEM61861.2024.10585601},
isbn = {979-835037453-7 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Gaming, Entertain., Media Conf., GEM},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In recent years, the application of synthetic humans in various fields has attracted considerable attention, leading to extensive exploration of their integration into the Metaverse and virtual production environments. This work presents a semi-automated approach that aims to find a fair trade-off between high-quality outputs and efficient production times. The project focuses on the Rai photo and video archives to find images of target characters for texturing and 3D reconstruction with the goal of reviving Rai's 2D footage and enhance the media experience. A key aspect of this study is to minimize the human intervention, ensuring an efficient, flexible, and scalable creation process. In this work, the improvements have been distributed among different stages of the digital human creation process, starting with the generation of 3D head meshes from 2D images of the reference character and then moving on to the generation, using a Diffusion model, of suitable images for texture development. These assets are then integrated into the Unreal Engine, where a custom widget facilitates posing, rendering, and texturing of Synthetic Humans models. Finally, an in-depth quantitative comparison and subjective tests were carried out between the original character images and the rendered synthetic humans, confirming the validity of the approach. © 2024 IEEE.},
keywords = {AI automation, Automation, Creation process, Digital humans, Economic and social effects, Extensive explorations, Face reconstruction, Generative AI, Image enhancement, media archive, Media archives, Metaverses, Rendering (computer graphics), Synthetic human, Synthetic Humans, Textures, Three dimensional computer graphics, Virtual production, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
In recent years, the application of synthetic humans in various fields has attracted considerable attention, leading to extensive exploration of their integration into the Metaverse and virtual production environments. This work presents a semi-automated approach that aims to find a fair trade-off between high-quality outputs and efficient production times. The project focuses on the Rai photo and video archives to find images of target characters for texturing and 3D reconstruction with the goal of reviving Rai's 2D footage and enhance the media experience. A key aspect of this study is to minimize the human intervention, ensuring an efficient, flexible, and scalable creation process. In this work, the improvements have been distributed among different stages of the digital human creation process, starting with the generation of 3D head meshes from 2D images of the reference character and then moving on to the generation, using a Diffusion model, of suitable images for texture development. These assets are then integrated into the Unreal Engine, where a custom widget facilitates posing, rendering, and texturing of Synthetic Humans models. Finally, an in-depth quantitative comparison and subjective tests were carried out between the original character images and the rendered synthetic humans, confirming the validity of the approach. © 2024 IEEE.