用扩散Transformer实现人脸表情到仿生机器人精准控制
ExFace: Expressive Facial Control for Humanoid Robots with Diffusion Transformers and Bootstrap Training
- 基于扩散Transformer建模人脸混合形状到机器人动作的映射
- 实测精度、帧率和响应速度均优于现有方法
- 适合需要自然表情交互的仿生机器人应用
本文提出一种基于扩散Transformer的表达式面部控制方法（ExFace），实现从人类面部混合形状到仿生机器人电机控制的精确映射。通过创新的模型自举训练策略，该方法不仅生成高质量面部表情，还显著提升准确性与平滑性。实验表明，所提方法在精度、每秒帧数（FPS）和响应时间上均优于先前方法。此外，我们构建了由人类面部数据驱动的ExFace数据集。ExFace在机器人表演和人机交互等场景中展现出出色的实时性能与自然表情渲染能力，为仿生机器人交互提供新方案。
原文摘要 · Abstract (English)
This paper presents a novel Expressive Facial Control (ExFace) method based on Diffusion Transformers, which achieves precise mapping from human facial blendshapes to bionic robot motor control. By incorporating an innovative model bootstrap training strategy, our approach not only generates high-quality facial expressions but also significantly improves accuracy and smoothness. Experimental results demonstrate that the proposed method outperforms previous methods in terms of accuracy, frame per second (FPS), and response time. Furthermore, we develop the ExFace dataset driven by human facial data. ExFace shows excellent real-time performance and natural expression rendering in applications such as robot performances and human-robot interactions, offering a new solution for bionic robot interaction.
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