Summary of Said: Speech-driven Blendshape Facial Animation with Diffusion, by Inkyu Park et al.
SAiD: Speech-driven Blendshape Facial Animation with Diffusion
by Inkyu Park, Jaewoong Cho
First submitted to arxiv on: 25 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 proposed SAiD (Speech-driven Animation with a diffusion model) is a novel approach for generating 3D facial animations from speech inputs. The method employs a lightweight Transformer-based U-Net architecture that incorporates cross-modality alignment biases between audio and visual features to enhance lip synchronization. This advancement addresses the limitations of existing methods, which typically rely on regression models trained on small datasets using least squares. SAiD’s performance is evaluated on the newly introduced BlendVOCA dataset, which provides a benchmark for assessing the quality of speech-driven facial animations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to make 3D faces move in sync with spoken words. This is important because making faces match what people are saying is a challenging task that requires a lot of effort and data. The new approach, called SAiD, uses a special kind of computer model that can learn from small amounts of data. This means it can generate a wide range of facial movements and make the editing process easier. The team also created a dataset called BlendVOCA to help others test their own facial animation methods. |
Keywords
* Artificial intelligence * Alignment * Diffusion model * Regression * Transformer
