AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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Here you can find the complete list of our publications.
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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
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.
2024
Xiao, Z.; Wang, T.; Wang, J.; Cao, J.; Zhang, W.; Dai, B.; Lin, D.; Pang, J.
UNIFIED HUMAN-SCENE INTERACTION VIA PROMPTED CHAIN-OF-CONTACTS Proceedings Article
In: Int. Conf. Learn. Represent., ICLR, International Conference on Learning Representations, ICLR, 2024.
Abstract | Links | BibTeX | Tags: Contact regions, Human joints, Interaction controls, Interaction framework, Quality control, Scene interactions, Strong correlation, Task executions, Task plan, Unified control, User friendly interface, Virtual Reality
@inproceedings{xiao_unified_2024,
title = {UNIFIED HUMAN-SCENE INTERACTION VIA PROMPTED CHAIN-OF-CONTACTS},
author = {Z. Xiao and T. Wang and J. Wang and J. Cao and W. Zhang and B. Dai and D. Lin and J. Pang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189112121&partnerID=40&md5=ed6c80431e6c18f32cdb9dd013fd60d0},
year = {2024},
date = {2024-01-01},
booktitle = {Int. Conf. Learn. Represent., ICLR},
publisher = {International Conference on Learning Representations, ICLR},
abstract = {Human-Scene Interaction (HSI) is a vital component of fields like embodied AI and virtual reality. Despite advancements in motion quality and physical plausibility, two pivotal factors, versatile interaction control and user-friendly interfaces, require further exploration for the practical application of HSI. This paper presents a unified HSI framework, named UniHSI, that supports unified control of diverse interactions through language commands. The framework defines interaction as “Chain of Contacts (CoC)”, representing steps involving human joint-object part pairs. This concept is inspired by the strong correlation between interaction types and corresponding contact regions. Based on the definition, UniHSI constitutes a Large Language Model (LLM) Planner to translate language prompts into task plans in the form of CoC, and a Unified Controller that turns CoC into uniform task execution. To support training and evaluation, we collect a new dataset named ScenePlan that encompasses thousands of task plans generated by LLMs based on diverse scenarios. Comprehensive experiments demonstrate the effectiveness of our framework in versatile task execution and generalizability to real scanned scenes. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Contact regions, Human joints, Interaction controls, Interaction framework, Quality control, Scene interactions, Strong correlation, Task executions, Task plan, Unified control, User friendly interface, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Human-Scene Interaction (HSI) is a vital component of fields like embodied AI and virtual reality. Despite advancements in motion quality and physical plausibility, two pivotal factors, versatile interaction control and user-friendly interfaces, require further exploration for the practical application of HSI. This paper presents a unified HSI framework, named UniHSI, that supports unified control of diverse interactions through language commands. The framework defines interaction as “Chain of Contacts (CoC)”, representing steps involving human joint-object part pairs. This concept is inspired by the strong correlation between interaction types and corresponding contact regions. Based on the definition, UniHSI constitutes a Large Language Model (LLM) Planner to translate language prompts into task plans in the form of CoC, and a Unified Controller that turns CoC into uniform task execution. To support training and evaluation, we collect a new dataset named ScenePlan that encompasses thousands of task plans generated by LLMs based on diverse scenarios. Comprehensive experiments demonstrate the effectiveness of our framework in versatile task execution and generalizability to real scanned scenes. © 2025 Elsevier B.V., All rights reserved.