Summary of Identity-preserving Pose-guided Character Animation Via Facial Landmarks Transformation, by Lianrui Mu et al.
Identity-Preserving Pose-Guided Character Animation via Facial Landmarks Transformation
by Lianrui Mu, Xingze Zhou, Wenjie Zheng, Jiangnan Ye, Haoji Hu
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 method called Facial Landmarks Transformation (FLT) to create realistic pose-guided image-to-video character animations while maintaining facial identity consistency. Existing methods struggle with misalignments between facial landmarks and reference images, leading to inaccurate results. FLT addresses this issue by leveraging a 3D Morphable Model to convert 2D landmarks into a 3D face model, align it with the reference identity, and then transform it back into 2D landmarks. This approach ensures accurate alignment with the reference facial geometry, enhancing the consistency between generated videos and reference images. The FLT method is evaluated through experimental results, demonstrating its effectiveness in preserving facial identity and improving pose-guided character animation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine creating movies or videos where characters can dance or move around with a real face. This paper shows how to make this happen by using a special technique called Facial Landmarks Transformation (FLT). Currently, it’s hard to keep the character’s face looking consistent when they’re moving and changing expressions. FLT helps solve this problem by matching the character’s facial features with a reference image. This ensures that the character’s face looks like their real-life counterpart throughout the video. The paper tests FLT and shows that it works well in creating realistic animations. |
Keywords
» Artificial intelligence » Alignment
