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Electronics
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AI for Human Collaboration
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AI for Human Collaboration

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 March 2025 | Viewed by 335

Special Issue Editor

Dr. Ji-Hyeong Han

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: AI and deep learning; intelligent robot; human–robot interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI and deep learning technologies have recently been developing really quickly and have spread to various domains. Generative AI technology specifically shows activity comparable to humans in some areas, such as writing, posing questions, answering, and drawing. Based on such a rapid and successful development of AI and deep learning technology, people use AI for assistance in their life and work. Thus, AI’s potential for not only assisting human efforts but also augmenting human cognitive and creative capabilities and fostering effective collaboration between humans and machines must be considered.

In this context, for this Special Issue on “AI for Human Collaboration”, we invite original research articles and comprehensive reviews covering but not limited to the following topics:

  • Collaborative AI systems;
  • AI for augmenting human abilities;
  • Ethical and societal implications of AI;
  • Human-centric AI design;
  • Any AI applications that assist humans.

Dr. Ji-Hyeong Han
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI
  • deep learning
  • machine learning
  • collaboration
  • human–computer interaction
  • human–machine interaction

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Published Papers (1 paper)

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Research

18 pages, 2568 KiB  
Article
ATGT3D: Animatable Texture Generation and Tracking for 3D Avatars
by Fei Chen and Jaeho Choi
Electronics 2024, 13(22), 4562; https://doi.org/10.3390/electronics13224562 - 20 Nov 2024
Viewed by 203
Abstract
We propose the ATGT3D an Animatable Texture Generation and Tracking for 3D Avatars, featuring the innovative design of the Eye Diffusion Module (EDM) and Pose Tracking Diffusion Module (PTDM), which are dedicated to high-quality eye texture generation and synchronized tracking of dynamic poses [...] Read more.
We propose the ATGT3D an Animatable Texture Generation and Tracking for 3D Avatars, featuring the innovative design of the Eye Diffusion Module (EDM) and Pose Tracking Diffusion Module (PTDM), which are dedicated to high-quality eye texture generation and synchronized tracking of dynamic poses and textures, respectively. Compared to traditional GAN and VAE methods, ATGT3D significantly enhances texture consistency and generation quality in animated scenes using the EDM, which produces high-quality full-body textures with detailed eye information using the HUMBI dataset. Additionally, the Pose Tracking and Diffusion Module (PTDM) monitors human motion parameters utilizing the BEAT2 and AMASS mesh-level animatable human model datasets. The EDM, in conjunction with a basic texture seed featuring eyes and the diffusion model, restores high-quality textures, whereas the PTDM, by integrating MoSh++ and SMPL-X body parameters, models hand and body movements from 2D human images, thus providing superior 3D motion capture datasets. This module maintains the synchronization of textures and movements over time to ensure precise animation texture tracking. During training, the ATGT3D model uses the diffusion model as the generative backbone to produce new samples. The EDM improves the texture generation process by enhancing the precision of eye details in texture images. The PTDM involves joint training for pose generation and animation tracking reconstruction. Textures and body movements are generated individually using encoded prompts derived from masked gestures. Furthermore, ATGT3D adaptively integrates texture and animation features using the diffusion model to enhance both fidelity and diversity. Experimental results show that ATGT3D achieves optimal texture generation performance and can flexibly integrate predefined spatiotemporal animation inputs to create comprehensive human animation models. Our experiments yielded unexpectedly positive outcomes. Full article
(This article belongs to the Special Issue AI for Human Collaboration)
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