Training-Free 3-D Face Avatars Generation by Knowledge Discovering in Foundational Models
Bibliographic Data
| ID | 22108684 |
|---|---|
| Authors | Qilei Li (0000-0002-9675-9016, Shandong University of Technology), Mingze Xu (0009-0004-5130-5741, Shandong University of Technology), Wenzhe Zhai (0000-0003-0996-6832, Shandong University of Technology), D Camacho (0000-0002-5051-3475, Universidad Politécnica de Madrid), Gwanggil Jeon (0000-0002-0651-4278, Shandong University of Technology) |
| Year | 2025 |
| Volume | 12 |
| Issue | 6 |
| Pages | 5481-5490 |
| Publication date | 2025-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2025.3554455 |
| OpenAlex | W4409249322 |
| Language | EN |
| Citations received | 1 |
| References cited | 27 |
An informative 3-D avatar, closely mirroring real-world traits, plays a pivotal role in accessing the metaverse. Traditional methods for creating 3-D avatars usually employ one-to-one training, which restricts avatar diversity. To enhance style diversity in generated 3-D avatars, we utilize synthesized images derived with prompts from ChatGPT in a conversational manner, ultimately resulting in a broader range of 3-D variations. Rather than creating models from scratch, we devise a training-free framework that utilizes established large-scale foundation models. Specifically, we employ a real-world image synthesis technique guided by text prompts that are generated by ChatGPT in a conversational manner, to describe the desired characteristics of the synthesized image. As a result, these informative latent representations can accurately reflect the distinct style of the synthesized image, and further lead to the creation of photorealistic and diverse 3D avatars. Our training-free design allows this proposed method to achieve competitive performance compared to existing generation models, while requiring minimal computational resources
Cognitive science · Geography · Natural language processing · Social science · Sociology · 3D Shape Modeling and Analysis · Augmented Reality Applications · Computer Science · Face recognition and analysis · Psychology · Artificial Intelligence
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |