Summary of Investigating the Impact Of 2d Gesture Representation on Co-speech Gesture Generation, by Teo Guichoux et al.
Investigating the impact of 2D gesture representation on co-speech gesture generation
by Teo Guichoux, Laure Soulier, Nicolas Obin, Catherine Pelachaud
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 investigates how the dimensionality of training data affects the performance of a deep generative model for co-speech gestures. Recent advances in deep learning have enabled realistic gesture generation, but these methods require large amounts of training data. To address this issue, researchers have turned to “in-the-wild” datasets that compile videos from sources like YouTube through human pose detection models. These datasets provide 2D skeleton sequences paired with speech. Innovative lifting models can transform these 2D sequences into their 3D counterparts, leading to large and diverse datasets of 3D gestures. However, the derived 3D pose estimation is a pseudo-ground truth, and the actual ground truth is the 2D motion data. This distinction raises questions about the impact of gesture representation dimensionality on the quality of generated motions. The authors evaluate the impact of training data dimensionality (2D or 3D joint coordinates) on the performance of a multimodal speech-to-gesture deep generative model. They use a lifting model to convert 2D-generated sequences of body pose to 3D, then compare the sequence of gestures generated directly in 3D to those generated in 2D and lifted to 3D as post-processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how the way we train AI models affects their ability to generate natural-looking gestures that match what people say. Right now, there are some pretty good AI models for this kind of thing, but they need a lot of data to learn from. One way researchers get more data is by using computers to analyze videos and extract information about people’s movements. This helps create big datasets of 2D skeleton sequences that match what people say. Some new techniques can even turn these 2D sequences into 3D ones, which is useful because it lets us make bigger and more diverse datasets. The authors are trying to figure out if the way we represent gestures – as 2D or 3D movements – affects how good our AI models are at generating natural-looking gestures. They’re using a special kind of model that can convert 2D sequences into 3D ones, then comparing how well it does when training with 2D data versus 3D data. |
Keywords
» Artificial intelligence » Deep learning » Generative model » Pose estimation
