Detangled：一个用于创建、编辑和推理具有丰富特征的发丝的框架
Detangled: A Framework for Creating, Editing, and Inferencing Feature Rich Hair Strands
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中文总结 AI 辅助
该研究提出用于理解和生成丰富特征发丝的框架，构建五维参数空间，用新方法分离发丝纹理与整体方向，通过生成式AI创建符合纹理描述的新发丝，可用于造型编辑并展示了多种发型结果。
中文摘要 AI 辅助
我们提出了一个用于理解和生成具有丰富特征的发丝的框架。借鉴科学和文化专业知识，我们将发丝纹理定义为由发丝内部力量形成的各种独特模式（卷曲、折返、扭转等）。我们首先提出了一个新颖的五维参数空间，旨在与自然存在的发丝纹理形成双射。这种编码在定性上易于理解，让用户能在参数空间中轻松定位自己的头发，在定量上也很精确，能根据纹理输入生成单个发丝。为了将发丝纹理与整体发丝方向分离，我们识别中心线几何形状并将发丝映射到规范空间（发丝纹理空间）。我们用一种新颖方法构建中心线，清晰地提炼复杂的头发造型，将发丝纹理与整体风格分离。通过由单独神经网络监督的生成式人工智能方法，我们能创建符合参数化纹理描述的新发丝，这使得能使用纹理转移或用户输入进行造型编辑。我们展示了在各种发型上的结果。
英文摘要
We present a framework for understanding and generating feature rich hair strands. Drawing upon both scientific and cultural expertise, we define strand texture as the various distinctive patterns (curling, switchbacks, twist, etc.) that are formed by forces internal to a hair strand. We begin by proposing a novel five-dimensional parameter space, intended to be a bijection with naturally occurring hair strand textures. This encoding is both qualitatively accessible, allowing users to readily locate their own hair in the parameter space, and quantitatively precise, allowing the generation of individual strands from texture inputs. Importantly, strand texture should be independent from the overall strand direction. In order to disentangle strand texture from the overall strand direction, we identify centerline geometry and use it to map strands into a canonical space (a strand texture space). We construct centerlines using a novel method that cleanly distills complex hair grooms, separating the strand texture from the overall style (parameterized by style guides). We enable the creation of new strands conforming to our parametric description of texture via a generative artificial intelligence approach supervised by a separate neural network trained to label candidate strands according to our five-parameter description. The ability to create new strands conforming to any desired texture enables groom editing using either texture transfer or user-provided inputs. We demonstrate results on a variety of hair types.
发表机构
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
