  Orthodontics focuses on rectifying misaligned teeth (i.e., malocclusions),
affecting both masticatory function and aesthetics. However, orthodontic
treatment often involves complex, lengthy procedures. As such, generating a 2D
photograph depicting aligned teeth prior to orthodontic treatment is crucial
for effective dentist-patient communication and, more importantly, for
encouraging patients to accept orthodontic intervention. In this paper, we
propose a 3D structure-guided tooth alignment network that takes 2D photographs
as input (e.g., photos captured by smartphones) and aligns the teeth within the
2D image space to generate an orthodontic comparison photograph featuring
aesthetically pleasing, aligned teeth. Notably, while the process operates
within a 2D image space, our method employs 3D intra-oral scanning models
collected in clinics to learn about orthodontic treatment, i.e., projecting the
pre- and post-orthodontic 3D tooth structures onto 2D tooth contours, followed
by a diffusion model to learn the mapping relationship. Ultimately, the aligned
tooth contours are leveraged to guide the generation of a 2D photograph with
aesthetically pleasing, aligned teeth and realistic textures. We evaluate our
network on various facial photographs, demonstrating its exceptional
performance and strong applicability within the orthodontic industry.
