We present PartNerFace, a part-based neural radiance fields approach, for reconstructing animatable facial avatar from monocular RGB videos. Existing solutions either simply condition the implicit network with the morphable model parameters or learn an imaginary canonical radiance field, making them fail to generalize to unseen facial expressions and capture fine-scale motion details. To address these challenges, we first apply inverse skinning based on a parametric head model to map an observed point to the canonical space, and then model fine-scale motions with a part-based deformation field. Our key insight is that the deformation of different facial parts should be modeled differently. Specifically, our part-based deformation field consists of multiple local MLPs to adaptively partition the canonical space into different parts, where the deformation of a 3D point is computed by aggregating the prediction of all local MLPs by a soft-weighting mechanism. Extensive experiments demonstrate that our method generalizes well to unseen expressions and is capable of modeling fine-scale facial motions, outperforming state-of-the-art methods both quantitatively and qualitatively.
Figures & tables
Figure 2: Overview of PartNerFace . Our method represents the facial avatar as a neural radiance fields in the canonical space. Given a 3D point in the posed space, it is first transformed to the canonical space through inverse LBS based on the FLAME model, then followed by a part assigner which predicts the probability of its part ascription. This probability is used to aggregate the output from a set of local deformation networks. The weighted deformation is then added to the previous transformed point. Finally, the occupancy and color is evaluated in the canonical space for volume rendering.
Figure 3: Qualitative comparison on the reconstruction of Testset . Some regions are zoomed in for detailed comparisons.
Figure 4: Visual comparison of animation results with unseen poses and expressions.
PSNR ↑
SSIM ↑
L1 ↓
LPIPS ↓
FOMM
22.22
0.87
0.085
0.035
SAFA
18.47
0.85
0.142
0.050
NerFace
26.77
0.93
0.051
0.012
IMavatar
23.78
0.91
0.073
0.018
Ours
25.73
0.93
0.056
0.012
Table 1: Quantitative Comparsion on the reconstruction of Testset . We report mean values for each metrics. Although the quantitative results of NerFace are slightly better than ours, we argue this is due to its overfitting on training data. However, when feed with unseen pose and expressions, its quantitative results declined by a large margin (see supplemental materials).
Figure 5: Qualitative comparison of ablation studies . Columns 1-5: the visual comparison of between different variants and ground truths. Columns 6-8: the visualized face partitions on the learned surface. We use separate colors for each part and use the brightness to indicate the probability of surface points belonging this part (for hard part assignment, its probability is all ones).
PSNR ↑
SSIM ↑
L1 ↓
LPIPS ↓
Global
25.30
0.92
0.059
0.014
Hard
25.60
0.92
0.061
0.013
Soft
25.49
0.92
0.057
0.013
Ours
25.73
0.93
0.056
0.012
Table 2: Quantitative comparsion of ablation studies .