Computer graphics algorithms for generating photorealistic imagery are widely perceived to be universal, and capable of conjuring anything that a filmmaker or game designer can imagine. However, recent works have suggested that 3D algorithms for depicting synthetic humans are far from generic, and instead favor historically hegemonic characteristics. We present the first systematic review of human depiction in the top computer graphics conference and the journal of record (SIGGRAPH and ACM Transactions on Graphics) that confirms previous hypotheses. Algorithms that claim to be generically rendering "human skin'' are in fact imagined and formulated for translucent, "high albedo" materials such as white skin. Algorithms claiming to apply generically to "human hair" are formulated for "rods", "wires" and "threads" which are analogous to straight hair. Our analysis reveals conceptual binarization, where algorithms for white skin are treated as computational substrate for "all" skin, imposing a hierarchical assumption that all skin descends from the math and physics of white skin. Hair algorithms follow a similar historical pattern, with the first examples of computer-generated Type 4 hair only appearing after the murder of George Floyd in 2020. We offer a new conceptual label, McDaniels Methods, for characterizing and critiquing computer graphics algorithms that reinforce racial hierarchy under a false cover of diversity. We also offer an inverse label, Durald Methods, for algorithms that were closely co-designed with the people being depicted. Our analysis points the way towards several neglected avenues for future research.

翻译：生成逼真图像的计算机图形学算法被广泛认为是通用的，能够构想电影制作人或游戏设计师所想象的任何事物。然而，近期研究表明，用于描绘合成人类的3D算法远非通用，反而偏向历史上占主导地位的特征。我们首次对顶级计算机图形学会议及权威期刊（SIGGRAPH和《ACM图形学汇刊》）中的人类描绘进行了系统性综述，确认了先前的假设。声称通用渲染“人类皮肤”的算法实际上是为半透明、“高反照率”材料（如白色皮肤）而设想和构建的。声称通用应用于“人类头发”的算法则针对“棒状”、“线状”和“丝状”结构（与直发类似）而设计。我们的分析揭示了一种概念上的二元化现象：处理白色皮肤的算法被视为所有皮肤的“计算基底”，从而强加了一种等级假设，即所有皮肤都源自于白色皮肤的数学和物理模型。头发算法遵循类似的历史模式，首例计算机生成的4型头发（即卷曲发）仅在2020年乔治·弗洛伊德被杀后才出现。我们提出了一个新的概念标签——“麦克丹尼尔方法”，用于描述和批判那些在虚假多样性掩护下强化种族等级的计算机图形学算法。同时，我们提出了一个相反的标签——“杜拉尔德方法”，用于描述那些与被描绘人群紧密协同设计的算法。我们的分析为未来研究指出了几个被忽视的方向。