# Multi-Level Feature Dynamic Fusion Neural Radiance Fields for Audio-Driven Talking Head Generation
## 4 Experiments
### Qualitative Evaluation  
For a qualitative comparison, we show some generated frames in Figure 4 and details of two portraits in Figure 5. For AD-NeRF [6], RAD-NeRF [11], ER-NeRF [12], and our proposed method, we generate the torso parts and subsequently evaluate them as a whole entity. In terms of lip sync--one-shot-based methods, such as Wav2 Lip [20], have shortcomings in personalization. Although such methods outperform other NeRF baselines in lip sync, they cannot maintain movements consistent with the target person's style. Due to the lack of blink control, Wav2 Lip sometimes produces strange eye movements. In contrast, our method can present more details and effectively maintain a personalized speaking style with high audio-visual synchronization.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Audio A**} & \multicolumn{2}{c}{**Audio B**} \\
**Methods** & **Sync\(\uparrow\)** & **LMD\(\downarrow\)** & **Sync\(\uparrow\)** & **LMD\(\downarrow\)** \\ \hline AD-NeRF & 4.559 & 4.351 & 5.395 & 2.587 \\ RAD-NeRF & 5.676 & 3.723 & 7.263 & 2.473 \\ GeneFace & 5.193 & 3.525 & 6.575 & 2.457 \\ ER-NeRF & 6.175 & 3.623 & 7.732 & 2.373 \\
**MLDF-NeRF** & **6.903** & **3.514** & **8.036** & **2.271** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The quantitative results of lip synchronization settings. The best value are in **bold**.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **PSNR\(\uparrow\)** & **LPIPS\(\downarrow\)** & **LMD\(\downarrow\)** & **FID\(\downarrow\)** \\ \hline AD-NeRF & 25.11 & 0.0909 & 3.249 & 20.65 \\ RAD-NeRF & 25.58 & 0.0764 & 2.733 & 11.06 \\ GeneFace & 23.72 & 0.0753 & 2.653 & 6.99 \\ ER-NeRF & 25.76 & **0.0456** & 2.594 & 5.70 \\
**MLDF-NeRF** & **26.27** & 0.0474 & **2.528** & **4.85** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The quantitative results of the overall portrait setting. The best values are in **bold**.  
During the head and torso matching process, AD-NeRF has a clear gap between the head and torso in the generated image. Despite alleviating the head-and-torso matching problem by RAD-NeRF and ER-NeRF, some generated images still exhibit position deviations, potentially due to numerical instability during backbone pose encoding. In comparison, our method has stronger stability and robustness, which may be due to the numerical stability of our adaptive pose encoder while performing efficient operations. This numerical stability can make the anchor point conversion and encoding of the torso-NeRF more efficient and reliable.  
**User study:** To further confirm the practical application of our proposed approach, we designed a set of user experiments. Specifically, we randomly selected 20 video clips from the videos generated by these methods in the quantitative evaluation experiment and invited 16 users to score them in terms of lip sync accuracy, image quality, and video authenticity. Figure 6 displays the statistical average score. The user study shows that the talking head videos generated by our method meet the visual quality and movements that humans perceive.