Summary of Towards Rich Emotions in 3d Avatars: a Text-to-3d Avatar Generation Benchmark, by Haidong Xu et al.
Towards Rich Emotions in 3D Avatars: A Text-to-3D Avatar Generation Benchmark
by Haidong Xu, Meishan Zhang, Hao Ju, Zhedong Zheng, Hongyuan Zhu, Erik Cambria, Min Zhang, Hao Fei
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 reexamines Emo3D generation, a critical research topic in 3D avatar creation, focusing on generating emotionally dynamic avatars from spoken words. The authors propose a novel approach, breaking down Emo3D generation into two steps: Text-to-3D Expression Mapping (T3DEM) and 3D Avatar Rendering (3DAR). T3DEM is the most crucial step, encompassing three key challenges: Expression Diversity, Emotion-Content Consistency, and Expression Fluidity. To address these challenges, the authors introduce a benchmark and present EmoAva, a large-scale dataset for T3DEM, comprising 15,000 text-to-3D expression mappings. They also develop metrics to evaluate models against these challenges. Furthermore, they propose the Continuous Text-to-Expression Generator, an autoregressive Conditional Variational Autoencoder enhanced with Latent Temporal Attention and Expression-wise Attention mechanisms. The Globally-informed Gaussian Avatar (GiGA) model is also presented, incorporating a global information mechanism into 3D Gaussian representations to capture subtle micro-expressions and seamless transitions between emotional states. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating 3D facial avatars that can express emotions. Right now, we have ways to generate general-purpose 3D avatars, but making them emotionally dynamic is still a challenge. The authors want to make it easier to create these emotional avatars by breaking down the process into two steps: understanding what someone means and turning that into a 3D avatar. They created a big dataset of text-to-3D expression mappings to help with this process and came up with some new ways to evaluate how well models do in generating emotional avatars. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Variational autoencoder
