Machine Learning often relies on massive amounts of data to capture relevant patterns. In contexts where experts are performing highly-skilled tasks, however, large datasets can be hard to create, and seemingly small miscalculations on the model’s part can be disproportionately problematic.<\/p>\n
Sarah Jobalia, a Stanford University PhD candidate, will focus on the design of virtual 3D hair as a case study to show how grounding AI pipelines in cultural, scientific and social knowledge produces more realistic, diverse and reliable results. Virtual hair, which is both computationally complex and culturally important to represent faithfully, is central to adding depth and nuance to the stories we see on screen. In her research, she draws upon interdisciplinary insights to define strand texture as the various distinctive patterns that are formed by forces internal to a hair strand, presenting a novel taxonomy for rigorously and concisely describing texture. Mirroring social understandings of hair, Jobalia disentangles this texture from the hair\u2019s overall style, which describes where the strands fall in relation to the head. Through learning and generating texture and style separately, she highlights two fundamental but separate aspects of hair, enabling generation that can capture the essence of desired hairstyles. This both yields fast generation of the curly and afro-textured hair types that previous works struggle to capture and allows for intuitive user interaction and compatibility with existing artist workflows. Broadly, these insights show how grounding AI tools in existing knowledge not only improves results but allows tools to support, rather than replace, experts.<\/p>\n
Sarah Jobalia is a PhD candidate in Stanford University’s Computer Science Department, advised by Ron Fedkiw. Her research centers on building more accessible tools for animation artists to represent characters of all backgrounds on screen. Specifically, through the study of hair- both on- and off-screen, Jobalia uses a combination of computer graphics and machine learning to automatically generate 3D hair styles from just a few images of any subject. By focusing on how hair type affects the way hair looks and moves, she builds tools that are equitably representative of all styles.<\/p>\n","protected":false},"excerpt":{"rendered":"
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