mlsanityData to 7 Oct 2026

Makeup Extraction of 3D Representation via Illumination-Aware Image Decomposition

arXiv 2302.13279 ↗First posted 26 Feb 2023cs.CV cs.GRcited by 8

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Abstract (arXiv)

Facial makeup enriches the beauty of not only real humans but also virtual characters; therefore, makeup for 3D facial models is highly in demand in productions. However, painting directly on 3D faces and capturing real-world makeup are costly, and extracting makeup from 2D images often struggles with shading effects and occlusions. This paper presents the first method for extracting makeup for 3D facial models from a single makeup portrait. Our method consists of the following three steps. First, we exploit the strong prior of 3D morphable models via regression-based inverse rendering to extract coarse materials such as geometry and diffuse/specular albedos that are represented in the UV space. Second, we refine the coarse materials, which may have missing pixels due to occlusions. We apply inpainting and optimization. Finally, we extract the bare skin, makeup, and an alpha matte from the diffuse albedo. Our method offers various applications for not only 3D facial models but also 2D portrait images. The extracted makeup is well-aligned in the UV space, from which we build a large-scale makeup dataset and a parametric makeup model for 3D faces. Our disentangled materials also yield robust makeup transfer and illumination-aware makeup interpolation/removal without a reference image.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 48 of its 73 references that are in the map. First posted on arXiv 26 Feb 2023.

Builds on

48 of 73 references.

References · 48

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36 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 3; 2026: 1.

8 papers.