AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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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); 14322315 (ISSN), (Publisher: Springer Science and Business Media Deutschland GmbH).
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=2de9f0dbdf9ea162871458c08e711c94},
doi = {10.1007/s00371-024-03316-3},
issn = {01782789 (ISSN); 14322315 (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. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Springer Science and Business Media Deutschland GmbH},
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. © 2025 Elsevier B.V., All rights reserved.
Tian, Y.; Li, X.; Cheng, Z.; Huang, Y.; Yu, T.
In: Sensors, vol. 25, no. 15, 2025, ISSN: 14248220 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).
Abstract | Links | BibTeX | Tags: 3D faces, 3d facial model, 3D facial models, 3D modeling, adaptation, adult, Article, Audience perception evaluation, benchmarking, controlled study, Cross-modal, Face generation, Facial modeling, facies, Feature extraction, feedback, feedback system, female, Geometry, High-fidelity, human, illumination, Immersive media, Lighting, male, movie, Neural radiance field, Neural Radiance Fields, perception, Quality control, Rendering (computer graphics), Semantics, sensor, Three dimensional computer graphics, Virtual production, Virtual Reality
@article{tian_design_2025,
title = {Design of Realistic and Artistically Expressive 3D Facial Models for Film AIGC: A Cross-Modal Framework Integrating Audience Perception Evaluation},
author = {Y. Tian and X. Li and Z. Cheng and Y. Huang and T. Yu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105013137724&doi=10.3390%2Fs25154646&partnerID=40&md5=8508a27b693f0857ce7cb58e97a2705c},
doi = {10.3390/s25154646},
issn = {14248220 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Sensors},
volume = {25},
number = {15},
abstract = {The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},
keywords = {3D faces, 3d facial model, 3D facial models, 3D modeling, adaptation, adult, Article, Audience perception evaluation, benchmarking, controlled study, Cross-modal, Face generation, Facial modeling, facies, Feature extraction, feedback, feedback system, female, Geometry, High-fidelity, human, illumination, Immersive media, Lighting, male, movie, Neural radiance field, Neural Radiance Fields, perception, Quality control, Rendering (computer graphics), Semantics, sensor, Three dimensional computer graphics, Virtual production, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. © 2025 Elsevier B.V., All rights reserved.
Xi, Z.; Yao, Z.; Huang, J.; Lu, Z. -Q.; Yan, H.; Mu, T. -J.; Wang, Z.; Xu, Q. -C.
TerraCraft: City-scale generative procedural modeling with natural languages Journal Article
In: Graphical Models, vol. 141, 2025, ISSN: 15240703 (ISSN), (Publisher: Elsevier Inc.).
Abstract | Links | BibTeX | Tags: 3D scene generation, 3D scenes, algorithm, Automation, City layout, City scale, data set, Diffusion Model, Game design, Geometry, High quality, Language, Language Model, Large datasets, Large language model, LLMs, Modeling languages, Natural language processing systems, Procedural modeling, Procedural models, Scene Generation, Three dimensional computer graphics, three-dimensional modeling, urban area, Virtual Reality
@article{xi_terracraft_2025,
title = {TerraCraft: City-scale generative procedural modeling with natural languages},
author = {Z. Xi and Z. Yao and J. Huang and Z. -Q. Lu and H. Yan and T. -J. Mu and Z. Wang and Q. -C. Xu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105012397682&doi=10.1016%2Fj.gmod.2025.101285&partnerID=40&md5=15a84050280e5015b1f7b1ef40c62100},
doi = {10.1016/j.gmod.2025.101285},
issn = {15240703 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Graphical Models},
volume = {141},
abstract = {Automated generation of large-scale 3D scenes presents a significant challenge due to the resource-intensive training and datasets required. This is in sharp contrast to the 2D counterparts that have become readily available due to their superior speed and quality. However, prior work in 3D procedural modeling has demonstrated promise in generating high-quality assets using the combination of algorithms and user-defined rules. To leverage the best of both 2D generative models and procedural modeling tools, we present TerraCraft, a novel framework for generating geometrically high-quality 3D city-scale scenes. By utilizing Large Language Models (LLMs), TerraCraft can generate city-scale 3D scenes from natural text descriptions. With its intuitive operation and powerful capabilities, TerraCraft enables users to easily create geometrically high-quality scenes readily for various applications, such as virtual reality and game design. We validate TerraCraft's effectiveness through extensive experiments and user studies, showing its superior performance compared to existing baselines. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Elsevier Inc.},
keywords = {3D scene generation, 3D scenes, algorithm, Automation, City layout, City scale, data set, Diffusion Model, Game design, Geometry, High quality, Language, Language Model, Large datasets, Large language model, LLMs, Modeling languages, Natural language processing systems, Procedural modeling, Procedural models, Scene Generation, Three dimensional computer graphics, three-dimensional modeling, urban area, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Automated generation of large-scale 3D scenes presents a significant challenge due to the resource-intensive training and datasets required. This is in sharp contrast to the 2D counterparts that have become readily available due to their superior speed and quality. However, prior work in 3D procedural modeling has demonstrated promise in generating high-quality assets using the combination of algorithms and user-defined rules. To leverage the best of both 2D generative models and procedural modeling tools, we present TerraCraft, a novel framework for generating geometrically high-quality 3D city-scale scenes. By utilizing Large Language Models (LLMs), TerraCraft can generate city-scale 3D scenes from natural text descriptions. With its intuitive operation and powerful capabilities, TerraCraft enables users to easily create geometrically high-quality scenes readily for various applications, such as virtual reality and game design. We validate TerraCraft's effectiveness through extensive experiments and user studies, showing its superior performance compared to existing baselines. © 2025 Elsevier B.V., All rights reserved.