One of the most challenging problems in audio-driven talking head generation is achieving high-fidelity detail while ensuring precise synchronization. Given only a single reference image, extracting meaningful identity attributes becomes even more challenging, often causing the network to mirror the facial and lip structures too closely. To address these issues, we introduce RADIO, a framework engineered to yield high-quality dubbed videos regardless of the pose or expression in reference images. The key is to modulate the decoder layers using latent space composed of audio and reference features. Additionally, we incorporate ViT blocks into the decoder to emphasize high-fidelity details, especially in the lip region. Our experimental results demonstrate that RADIO displays high synchronization without the loss of fidelity. Especially in harsh scenarios where the reference frame deviates significantly from the ground truth, our method outperforms state-of-the-art methods, highlighting its robustness.

翻译：摘要：在音频驱动的说话人视频生成中，最具挑战性的问题之一是同步精确度的同时实现高保真细节。仅凭单张参考图像，提取有意义的身份属性变得尤为困难，常常导致网络过度模仿面部和嘴唇结构。为解决这些问题，我们提出了RADIO框架，无论参考图像中姿态或表情如何，都能生成高质量的配音视频。其关键是通过由音频和参考特征构成的潜在空间来调节解码器层。此外，我们在解码器中融入ViT模块以强调高保真细节，尤其是嘴唇区域。实验结果表明，RADIO在保持保真度的同时实现了高同步精度。尤其在参考帧与真实情况偏差较大的严苛场景下，我们的方法优于现有最佳技术，凸显了其鲁棒性。