MARBLE：CLIP 空间中的材质重组与混合
MARBLE: Material Recomposition and Blending in CLIP-Space
AI总结：
MARBLE 通过在 CLIP 空间中定位材质嵌入并识别 UNet 中负责材质归因的模块，实现基于示例的材质混合与细粒度属性（如粗糙度、金属感、透明度、发光）的参数化控制编辑。
摘要：
基于示例图像编辑图像中物体的材质是计算机视觉与图形学中一个活跃的研究领域。我们提出 MARBLE，一种通过寻找 CLIP 空间中的材质嵌入并利用其控制预训练文本到图像模型来执行材质混合和细粒度材质属性重组的方法。我们通过定位去噪 UNet 中负责材质归因的模块来改进基于示例的材质编辑。给定两张材质示例图像，我们在 CLIP 空间中寻找用于混合材质的方向。此外，我们能够使用一个浅层网络预测所需材质属性变化的方向，从而实现对粗糙度、金属感、透明度和发光等细粒度材质属性的参数化控制。我们进行了定性和定量分析，以证明所提方法的有效性。我们还展示了该方法在单次前向传播中执行多次编辑的能力以及对绘画的适用性。
英文摘要：
Editing materials of objects in images based on exemplar images is an active area of research in computer vision and graphics. We propose MARBLE, a method for performing material blending and recomposing fine-grained material properties by finding material embeddings in CLIP-space and using that to control pre-trained text-to-image models. We improve exemplar-based material editing by finding a block in the denoising UNet responsible for material attribution. Given two material exemplar-images, we find directions in the CLIP-space for blending the materials. Further, we can achieve parametric control over fine-grained material attributes such as roughness, metallic, transparency, and glow using a shallow network to predict the direction for the desired material attribute change. We perform qualitative and quantitative analysis to demonstrate the efficacy of our proposed method. We also present the ability of our method to perform multiple edits in a single forward pass and applicability to painting. Project Page: https://marblecontrol.github.io/
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。
