Summary of Semuv: Deep Learning Based Semantic Manipulation Over Uv Texture Map Of Virtual Human Heads, by Anirban Mukherjee et al.
SemUV: Deep Learning based semantic manipulation over UV texture map of virtual human heads
by Anirban Mukherjee, Venkat Suprabath Bitra, Vignesh Bondugula, Tarun Reddy Tallapureddy, Dinesh Babu Jayagopi
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces SemUV, a novel approach for semantic manipulation within the UV texture space of virtual human heads. The focus is on enhancing control and precision in appearance modification, crucial for applications like AR, VR, gaming, human-computer interaction, and VFX. While deep learning techniques excel at generating photorealistic 2D facial images, they are less suitable for 3D graphics. SemUV addresses this limitation by training a StyleGAN model on the FFHQ-UV dataset, enabling semantic feature manipulation within the UV texture space. The method is simple, agnostic to other 3D components, and integrates seamlessly into standard pipelines without requiring extensive domain expertise or resources. Experiments demonstrate SemUV’s superiority in preserving identity while modifying age, gender, and facial hair features compared to traditional 2D manipulation techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to change someone’s appearance in a virtual world. This paper is about creating a way to do just that. Right now, making realistic changes to virtual human heads requires a lot of work and technical expertise. The authors want to make it easier for graphic designers to control how people look in virtual environments. They developed a new method called SemUV, which uses special computer algorithms to manipulate the UV texture space of virtual faces. This allows for more precise and efficient editing. The results show that their approach is better than traditional methods at preserving the person’s identity while changing their appearance. |
Keywords
» Artificial intelligence » Deep learning » Precision
