# Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks, Methods, and Applications

**Authors:** Karren D. Yang, Anurag Ranjan, Jen-Hao Rick Chang, Raviteja Vemulapalli, Oncel Tuzel  
**arXiv:** [2311.18168](https://arxiv.org/abs/2311.18168)  
**Date:** 2023-11-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.18168v1/

## Key points

- Introduces a novel approach to animating 3D facial geometry from speech signals
- Addresses limitations in the field, such as small datasets and deterministic models
- Proposed probabilistic model achieves diversity and fidelity to speech
- Outperforms existing methods across benchmark datasets
- Showcases practical applications, including generating diverse facial motion and enhancing audio-visual models
- Highlights the importance of probabilistic modeling in capturing the relationship between speech and facial motion

## Summary

The paper titled "Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks, Methods, and Applications" introduces a novel approach to animating 3D facial geometry from speech signals using probabilistic modeling techniques. This addresses previous limitations in the field, such as small datasets and deterministic models that fail to capture the full range of diverse facial motions accompanying speech in real-world scenarios. The proposed probabilistic model successfully achieves both diversity and fidelity to speech, outperforming existing methods across benchmark datasets specifically tailored for this type of modeling. Additionally, practical applications are showcased, including generating diverse facial motion matching unseen speaker styles and enhancing audio-visual models with synthetic meshes generated by the model. Overall, this paper highlights the importance of probabilistic modeling in accurately capturing the relationship between speech and facial motion.