Photorealistic, animatable digital humans are valuable across industries such as fashion, healthcare, and entertainment, yet generating high-quality models remains complex and time-consuming. While parametric models like SMPL-X enable automatic body reconstruction from images, they lack texture and rigging, limiting their use in modern rendering engines. This paper presents a lightweight pipeline that combines scan-based texture transfer, semantic material labeling, and skeleton insertion to enhance SMPL-X models with photorealistic detail and full animation capability. Body regions are segmented using skinning weights, then refined and classified with a neural network to identify material types. The resulting digital avatar can be exported in standard formats like OBJ or FBX. With the help of the generated labels it is possible to accurately animate garments and simulation clothing on generated body models.
Meyer et al. (Wed,) studied this question.
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