  In recent years, generative 3D face models (e.g., EG3D) have been developed
to tackle the problem of synthesizing photo-realistic faces. However, these
models are often unable to capture facial features unique to each individual,
highlighting the importance of personalization. Some prior works have shown
promise in personalizing generative face models, but these studies primarily
focus on 2D settings. Also, these methods require both fine-tuning and storing
a large number of parameters for each user, posing a hindrance to achieving
scalable personalization. Another challenge of personalization is the limited
number of training images available for each individual, which often leads to
overfitting when using full fine-tuning methods. Our proposed approach,
My3DGen, generates a personalized 3D prior of an individual using as few as 50
training images. My3DGen allows for novel view synthesis, semantic editing of a
given face (e.g. adding a smile), and synthesizing novel appearances, all while
preserving the original person's identity. We decouple the 3D facial features
into global features and personalized features by freezing the pre-trained EG3D
and training additional personalized weights through low-rank decomposition. As
a result, My3DGen introduces only $\textbf{240K}$ personalized parameters per
individual, leading to a $\textbf{127}\times$ reduction in trainable parameters
compared to the $\textbf{30.6M}$ required for fine-tuning the entire parameter
space. Despite this significant reduction in storage, our model preserves
identity features without compromising the quality of downstream applications.
