TextureDreamer: Image-guided Texture Synthesis through Geometry-aware
  Diffusion
Yu-Ying Yeh Jia-Bin Huang Changil Kim Lei Xiao Thu Nguyen-Phuoc Numair Khan Cheng Zhang Manmohan Chandraker Carl S Marshall Zhao Dong Zhengqin Li
We present TextureDreamer, a novel image-guided texture synthesis method to
transfer relightable textures from a small number of input images (3 to 5) to
target 3D shapes across arbitrary categories. Texture creation is a pivotal
challenge in vision and graphics. Industrial companies hire experienced artists
to manually craft textures for 3D assets. Classical methods require densely
sampled views and accurately aligned geometry, while learning-based methods are
confined to category-specific shapes within the dataset. In contrast,
TextureDreamer can transfer highly detailed, intricate textures from real-world
environments to arbitrary objects with only a few casually captured images,
potentially significantly democratizing texture creation. Our core idea,
personalized geometry-aware score distillation (PGSD), draws inspiration from
recent advancements in diffuse models, including personalized modeling for
texture information extraction, variational score distillation for detailed
appearance synthesis, and explicit geometry guidance with ControlNet. Our
integration and several essential modifications substantially improve the
texture quality. Experiments on real images spanning different categories show
that TextureDreamer can successfully transfer highly realistic, semantic
meaningful texture to arbitrary objects, surpassing the visual quality of
previous state-of-the-art.