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.
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.
2024
Kumaar, D. Prasanna; Abisha, D.; Christy, J. Ida; Evanjalin, R. Navedha; Rajesh, P.; Haris, K. Mohamed
WRISTVIEW: Augmented Reality and Generative AI Integration for Enhanced Online Shopping Experiences Proceedings Article
In: Int. Conf. I-SMAC (IoT Soc., Mob., Anal. Cloud), I-SMAC - Proc., pp. 1115–1120, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835037642-5 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented Reality, Chatbots, Conversational AI, Customer satisfaction, Decision making, E-commerce Innovation, Growing demand, Immersive, Interactivity, Online shopping, Personalizations, Personalized Shopping Experience, Purchasing, Sales, Virtual environments, Virtual Try-On
@inproceedings{prasanna_kumaar_wristview_2024,
title = {WRISTVIEW: Augmented Reality and Generative AI Integration for Enhanced Online Shopping Experiences},
author = {D. Prasanna Kumaar and D. Abisha and J. Ida Christy and R. Navedha Evanjalin and P. Rajesh and K. Mohamed Haris},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208587489&doi=10.1109%2fI-SMAC61858.2024.10714789&partnerID=40&md5=e7742c8808cb551b17efe9ac3efeb961},
doi = {10.1109/I-SMAC61858.2024.10714789},
isbn = {979-835037642-5 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Int. Conf. I-SMAC (IoT Soc., Mob., Anal. Cloud), I-SMAC - Proc.},
pages = {1115–1120},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The traditional retail experience for purchasing watches lacks the interactivity and personalization that modern consumers seek. With the increasing shift towards online shopping platforms, there is a growing demand for an engaging and immersive virtual experience that allows customers to explore and try on watches. The absence of expert guidance during the online shopping process often results in less informed decision-making. To address these challenges, WRISTVIEW presents an innovative solution that integrates augmented reality (AR) and a conversational Generative AI (GENAI) chatbot. The AR component enables users to virtually try on watches, providing a realistic and interactive experience. The GENAI chatbot enhances this experience by offering expert advice, answering queries, and guiding users through the watch shopping journey, thereby creating a more personalized and informative shopping process. The objective of this research is to bridge the gap between the traditional in-store watch shopping experience and the online environment, ensuring that customers can make well-informed and satisfying purchase decisions in a virtual setting. The development, implementation, and potential impact of combining AR and GENAI technologies to transform the online watch shopping experience are discussed. © 2024 IEEE.},
keywords = {Augmented Reality, Chatbots, Conversational AI, Customer satisfaction, Decision making, E-commerce Innovation, Growing demand, Immersive, Interactivity, Online shopping, Personalizations, Personalized Shopping Experience, Purchasing, Sales, Virtual environments, Virtual Try-On},
pubstate = {published},
tppubtype = {inproceedings}
}
The traditional retail experience for purchasing watches lacks the interactivity and personalization that modern consumers seek. With the increasing shift towards online shopping platforms, there is a growing demand for an engaging and immersive virtual experience that allows customers to explore and try on watches. The absence of expert guidance during the online shopping process often results in less informed decision-making. To address these challenges, WRISTVIEW presents an innovative solution that integrates augmented reality (AR) and a conversational Generative AI (GENAI) chatbot. The AR component enables users to virtually try on watches, providing a realistic and interactive experience. The GENAI chatbot enhances this experience by offering expert advice, answering queries, and guiding users through the watch shopping journey, thereby creating a more personalized and informative shopping process. The objective of this research is to bridge the gap between the traditional in-store watch shopping experience and the online environment, ensuring that customers can make well-informed and satisfying purchase decisions in a virtual setting. The development, implementation, and potential impact of combining AR and GENAI technologies to transform the online watch shopping experience are discussed. © 2024 IEEE.
He, K.; Yao, K.; Zhang, Q.; Yu, J.; Liu, L.; Xu, L.
DressCode: Autoregressively Sewing and Generating Garments from Text Guidance Journal Article
In: ACM Transactions on Graphics, vol. 43, no. 4, 2024, ISSN: 07300301 (ISSN).
Abstract | Links | BibTeX | Tags: 3D content, 3d garments, autoregressive model, Autoregressive modelling, Content creation, Digital humans, Embeddings, Fashion design, Garment generation, Interactive computer graphics, Sewing pattern, sewing patterns, Textures, Virtual Reality, Virtual Try-On
@article{he_dresscode_2024,
title = {DressCode: Autoregressively Sewing and Generating Garments from Text Guidance},
author = {K. He and K. Yao and Q. Zhang and J. Yu and L. Liu and L. Xu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199257820&doi=10.1145%2f3658147&partnerID=40&md5=8996e62e4d9dabb5a7034f8bf4df5a43},
doi = {10.1145/3658147},
issn = {07300301 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {ACM Transactions on Graphics},
volume = {43},
number = {4},
abstract = {Apparel's significant role in human appearance underscores the importance of garment digitalization for digital human creation. Recent advances in 3D content creation are pivotal for digital human creation. Nonetheless, garment generation from text guidance is still nascent. We introduce a text-driven 3D garment generation framework, DressCode, which aims to democratize design for novices and offer immense potential in fashion design, virtual try-on, and digital human creation. We first introduce SewingGPT, a GPT-based architecture integrating cross-attention with text-conditioned embedding to generate sewing patterns with text guidance. We then tailor a pre-trained Stable Diffusion to generate tile-based Physically-based Rendering (PBR) textures for the garments. By leveraging a large language model, our framework generates CG-friendly garments through natural language interaction. It also facilitates pattern completion and texture editing, streamlining the design process through user-friendly interaction. This framework fosters innovation by allowing creators to freely experiment with designs and incorporate unique elements into their work. With comprehensive evaluations and comparisons with other state-of-the-art methods, our method showcases superior quality and alignment with input prompts. User studies further validate our high-quality rendering results, highlighting its practical utility and potential in production settings. Copyright © 2024 held by the owner/author(s).},
keywords = {3D content, 3d garments, autoregressive model, Autoregressive modelling, Content creation, Digital humans, Embeddings, Fashion design, Garment generation, Interactive computer graphics, Sewing pattern, sewing patterns, Textures, Virtual Reality, Virtual Try-On},
pubstate = {published},
tppubtype = {article}
}
Apparel's significant role in human appearance underscores the importance of garment digitalization for digital human creation. Recent advances in 3D content creation are pivotal for digital human creation. Nonetheless, garment generation from text guidance is still nascent. We introduce a text-driven 3D garment generation framework, DressCode, which aims to democratize design for novices and offer immense potential in fashion design, virtual try-on, and digital human creation. We first introduce SewingGPT, a GPT-based architecture integrating cross-attention with text-conditioned embedding to generate sewing patterns with text guidance. We then tailor a pre-trained Stable Diffusion to generate tile-based Physically-based Rendering (PBR) textures for the garments. By leveraging a large language model, our framework generates CG-friendly garments through natural language interaction. It also facilitates pattern completion and texture editing, streamlining the design process through user-friendly interaction. This framework fosters innovation by allowing creators to freely experiment with designs and incorporate unique elements into their work. With comprehensive evaluations and comparisons with other state-of-the-art methods, our method showcases superior quality and alignment with input prompts. User studies further validate our high-quality rendering results, highlighting its practical utility and potential in production settings. Copyright © 2024 held by the owner/author(s).