Summary of Diffspeaker: Speech-driven 3d Facial Animation with Diffusion Transformer, by Zhiyuan Ma et al.
DiffSpeaker: Speech-Driven 3D Facial Animation with Diffusion Transformer
by Zhiyuan Ma, Xiangyu Zhu, Guojun Qi, Chen Qian, Zhaoxiang Zhang, Zhen Lei
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a novel approach to speech-driven 3D facial animation using a Transformer-based network called DiffSpeaker. The authors draw inspiration from both diffusion models and Transformer architectures, but instead of simply aggregating them, they introduce biased conditional attention modules to steer the attention mechanisms towards relevant task-specific and diffusion-related conditions. This allows for improved performance on existing benchmarks while also achieving fast inference speeds due to parallel facial motion generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create realistic 3D animations that mimic real people’s faces. This is what this paper is all about! The researchers created a new computer program called DiffSpeaker, which can generate 3D facial animations based on audio input. They did this by combining two different approaches: one called diffusion models and another called Transformer architectures. By adding some special features to these methods, they were able to make the animations look more realistic and smooth. This could be useful for things like movies or video games. |
Keywords
» Artificial intelligence » Attention » Diffusion » Inference » Transformer
