Summary of Fast Registration Of Photorealistic Avatars For Vr Facial Animation, by Chaitanya Patel et al.
Fast Registration of Photorealistic Avatars for VR Facial Animation
by Chaitanya Patel, Shaojie Bai, Te-Li Wang, Jason Saragih, Shih-En Wei
First submitted to arxiv on: 19 Jan 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 The paper presents a novel system for annotating photorealistic avatars in virtual reality (VR) environments. The challenge lies in efficiently and accurately labeling images captured by headset-mounted cameras (HMCs), while wearing VR headsets, considering oblique camera views and differences in image modality. A transformer-based architecture is shown to be effective on domain-consistent data but degrades when the domain gap is introduced. To overcome this, a two-part system is proposed: an iterative refinement module and a generic avatar-guided image-to-image domain transfer module. The modules reinforce each other, allowing for online registration of higher quality than direct regression methods. The system obviates the need for offline optimization and demonstrates significant improvements over baselines through extensive experiments on commodity headsets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists have created a new way to help computers understand what people look like in virtual reality (VR). They want to make sure that these computers can accurately recognize and label pictures of people wearing VR headsets. This is tricky because the cameras on these headsets take weird angles and different kinds of pictures. The researchers used special computer programs to try and solve this problem, but they found that it’s hard to get good results when the pictures are from a different “world”. To fix this, they came up with a two-step plan: one part refines what it already knows, and another part uses that information to help it learn about new things. This makes it much better at recognizing people in VR! The scientists tested their system on real headsets and showed that it works way better than the old ways. |
Keywords
» Artificial intelligence » Optimization » Regression » Transformer
