We propose INFAMOUS-NeRF, an implicit morphable face model that introduces
hypernetworks to NeRF to improve the representation power in the presence of
many training subjects. At the same time, INFAMOUS-NeRF resolves the classic
hypernetwork tradeoff of representation power and editability by learning
semantically-aligned latent spaces despite the subject-specific models, all
without requiring a large pretrained model. INFAMOUS-NeRF further introduces a
novel constraint to improve NeRF rendering along the face boundary. Our
constraint can leverage photometric surface rendering and multi-view
supervision to guide surface color prediction and improve rendering near the
surface. Finally, we introduce a novel, loss-guided adaptive sampling method
for more effective NeRF training by reducing the sampling redundancy. We show
quantitatively and qualitatively that our method achieves higher representation
power than prior face modeling methods in both controlled and in-the-wild
settings. Code and models will be released upon publication.

我们提出了 INFAMOUS-NeRF，一种隐式可塑性人脸模型，通过引入超网络到 NeRF 中，改善在训练多个主题时的表示能力。同时，INFAMOUS-NeRF 通过学习语义对齐的潜在空间来解决了经典超网络在表示能力和可编辑性之间的权衡问题，而无需大规模预训练模型。INFAMOUS-NeRF 还引入了一种改进 NeRF 渲染的新约束，该约束可以利用光度表面渲染和多视角监督来指导表面颜色预测并改善表面附近的渲染。最后，我们引入了一种新的、通过减少采样冗余来实现更有效 NeRF 训练的损失引导自适应采样方法。我们定量和定性地展示了我们的方法在受控和野外环境中实现了比先前人脸建模方法更高的表示能力。代码和模型将在发表后发布。