  Creating photorealistic avatars for individuals traditionally involves
extensive capture sessions with complex and expensive studio devices like the
LightStage system. While recent strides in neural representations have enabled
the generation of photorealistic and animatable 3D avatars from quick phone
scans, they have the capture-time lighting baked-in, lack facial details and
have missing regions in areas such as the back of the ears. Thus, they lag in
quality compared to studio-captured avatars. In this paper, we propose a method
that bridges this gap by generating studio-like illuminated texture maps from
short, monocular phone captures. We do this by parameterizing the phone texture
maps using the $W^+$ space of a StyleGAN2, enabling near-perfect
reconstruction. Then, we finetune a StyleGAN2 by sampling in the $W^+$
parameterized space using a very small set of studio-captured textures as an
adversarial training signal. To further enhance the realism and accuracy of
facial details, we super-resolve the output of the StyleGAN2 using carefully
designed diffusion model that is guided by image gradients of the
phone-captured texture map. Once trained, our method excels at producing
studio-like facial texture maps from casual monocular smartphone videos.
Demonstrating its capabilities, we showcase the generation of photorealistic,
uniformly lit, complete avatars from monocular phone captures. The project page
can be found at http://shahrukhathar.github.io/2024/07/22/Bridging.html
