Lee, SeunghwanLiu, C. KarenSkouras, MelinaWang, He2024-08-202024-08-2020241467-8659https://doi.org/10.1111/cgf.15177https://diglib.eg.org/handle/10.1111/cgf15177Learning-based methods for 3D content generation have shown great potential to create 3D characters from text prompts, videos, and images. However, current methods primarily focus on generating static 3D meshes, overlooking the crucial aspect of creating an animatable 3D meshes. Directly using 3D meshes generated by existing methods to create underlying skeletons for animation presents many challenges because the generated mesh might exhibit geometry artifacts or assume arbitrary poses that complicate the subsequent rigging process. This work proposes a new framework for generating a 3D animatable mesh from a single 2D image depicting the character. We do so by enforcing the generated 3D mesh to assume an A-pose, which can mitigate the geometry artifacts and facilitate the use of existing automatic rigging methods. Our approach aims to leverage the generative power of existing models across modalities without the need for new data or large-scale training. We evaluate the effectiveness of our framework with qualitative results, as well as ablation studies and quantitative comparisons with existing 3D mesh generation models.CCS Concepts: Computing methodologies → Computer vision; RenderingComputing methodologies → Computer visionRendering10.1111/cgf.1517711 pages