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

TL;DR
TextureDreamer is a new method that enables realistic, detailed texture transfer to 3D shapes from only a few casual images, using geometry-aware diffusion techniques to outperform previous approaches.
Contribution
It introduces PGSD, a novel geometry-aware score distillation method that allows high-quality texture synthesis from minimal input images across arbitrary categories.
Findings
Successfully transfers detailed textures to diverse 3D objects.
Outperforms previous state-of-the-art in visual quality.
Works with only 3-5 casual images.
Abstract
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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis
