  This paper presents a novel method to generate textures for 3D models given
text prompts and 3D meshes. Additional depth information is taken into account
to perform the Score Distillation Sampling (SDS) process with depth conditional
Stable Diffusion. We ran our model over the open-source dataset Objaverse and
conducted a user study to compare the results with those of various 3D
texturing methods. We have shown that our model can generate more satisfactory
results and produce various art styles for the same object. In addition, we
achieved faster time when generating textures of comparable quality. We also
conduct thorough ablation studies of how different factors may affect
generation quality, including sampling steps, guidance scale, negative prompts,
data augmentation, elevation range, and alternatives to SDS.
