HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866909470362173440 |
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| author | Sevastopolsky, Artem Grassal, Philip-William Giebenhain, Simon Athar, ShahRukh Verdoliva, Luisa Niessner, Matthias |
| author_facet | Sevastopolsky, Artem Grassal, Philip-William Giebenhain, Simon Athar, ShahRukh Verdoliva, Luisa Niessner, Matthias |
| contents | Current advances in human head modeling allow the generation of plausible-looking 3D head models via neural representations, such as NeRFs and SDFs. Nevertheless, constructing complete high-fidelity head models with explicitly controlled animation remains an issue. Furthermore, completing the head geometry based on a partial observation, e.g., coming from a depth sensor, while preserving a high level of detail is often problematic for the existing methods. We introduce a generative model for detailed 3D head meshes on top of an articulated 3DMM, simultaneously allowing explicit animation and high-detail preservation. Our method is trained in two stages. First, we register a parametric head model with vertex displacements to each mesh of the recently introduced NPHM dataset of accurate 3D head scans. The estimated displacements are baked into a hand-crafted UV layout. Second, we train a StyleGAN model to generalize over the UV maps of displacements, which we later refer to as HeadCraft. The decomposition of the parametric model and high-quality vertex displacements allows us to animate the model and modify the regions semantically. We demonstrate the results of unconditional sampling, fitting to a scan and editing. The project page is available at https://seva100.github.io/headcraft. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_14140 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs Sevastopolsky, Artem Grassal, Philip-William Giebenhain, Simon Athar, ShahRukh Verdoliva, Luisa Niessner, Matthias Computer Vision and Pattern Recognition Current advances in human head modeling allow the generation of plausible-looking 3D head models via neural representations, such as NeRFs and SDFs. Nevertheless, constructing complete high-fidelity head models with explicitly controlled animation remains an issue. Furthermore, completing the head geometry based on a partial observation, e.g., coming from a depth sensor, while preserving a high level of detail is often problematic for the existing methods. We introduce a generative model for detailed 3D head meshes on top of an articulated 3DMM, simultaneously allowing explicit animation and high-detail preservation. Our method is trained in two stages. First, we register a parametric head model with vertex displacements to each mesh of the recently introduced NPHM dataset of accurate 3D head scans. The estimated displacements are baked into a hand-crafted UV layout. Second, we train a StyleGAN model to generalize over the UV maps of displacements, which we later refer to as HeadCraft. The decomposition of the parametric model and high-quality vertex displacements allows us to animate the model and modify the regions semantically. We demonstrate the results of unconditional sampling, fitting to a scan and editing. The project page is available at https://seva100.github.io/headcraft. |
| title | HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.14140 |