TextureDreamer：通过几何感知扩散实现图像引导的纹理合成
TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion
AI总结：
TextureDreamer提出个性化几何感知分数蒸馏（PGSD），仅用3到5张随意拍摄的图像即可将真实环境中的精细可重光照纹理迁移到任意3D物体上，显著超越此前方法。
摘要：
我们提出TextureDreamer，一种新颖的图像引导纹理合成方法，可将可重光照纹理从少量输入图像（3到5张）迁移到任意类别的目标3D形状上。纹理创建是视觉与图形学中的关键挑战。工业公司聘请经验丰富的艺术家为3D资产手工制作纹理。经典方法需要密集采样的视图和精确对齐的几何，而基于学习的方法局限于数据集内特定类别的形状。相比之下，TextureDreamer仅凭几张随意拍摄的图像，就能将真实环境中高度精细、复杂的纹理迁移到任意物体上，有望显著降低纹理创作的门槛。我们的核心思想是个性化几何感知分数蒸馏（PGSD），其灵感来源于扩散模型的最新进展，包括用于纹理信息提取的个性化建模、用于精细外观合成的变分分数蒸馏，以及利用ControlNet的显式几何引导。我们的整合及若干关键修改大幅提升了纹理质量。在涵盖不同类别的真实图像上的实验表明，TextureDreamer能够成功地将高度逼真、语义上有意义的纹理迁移到任意物体上，超越了先前最先进方法的视觉质量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
