{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/animatable-gaussians-learning-pose-dependent-1","title":"Animatable Gaussians: Learning Pose-dependent Gaussian Maps for High-fidelity Human Avatar Modeling","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Zhe Li","Zerong Zheng","Lizhen Wang","Yebin Liu"],"abstract":"    Modeling animatable human avatars from RGB videos is a long-standing and challenging problem. Recent works usually adopt MLP-based neural radiance fields (NeRF) to represent 3D humans but it remains difficult for pure MLPs to regress pose-dependent garment details. To this end we introduce Animatable Gaussians a new avatar representation that leverages powerful 2D CNNs and 3D Gaussian splatting to create high-fidelity avatars. To associate 3D Gaussians with the animatable avatar we learn a parametric template from the input videos and then parameterize the template on two front & back canonical Gaussian maps where each pixel represents a 3D Gaussian. The learned template is adaptive to the wearing garments for modeling looser clothes like dresses. Such template-guided 2D parameterization enables us to employ a powerful StyleGAN-based CNN to learn the pose-dependent Gaussian maps for modeling detailed dynamic appearances. Furthermore we introduce a pose projection strategy for better generalization given novel poses. Overall our method can create lifelike avatars with dynamic realistic and generalized appearances. Experiments show that our method outperforms other state-of-the-art approaches. Code: https://github.com/lizhe00/AnimatableGaussians.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Li_Animatable_Gaussians_Learning_Pose-dependent_Gaussian_Maps_for_High-fidelity_Human_Avatar_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Li_Animatable_Gaussians_Learning_Pose-dependent_Gaussian_Maps_for_High-fidelity_Human_Avatar_CVPR_2024_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"animatable-gaussians-learning-pose-dependent-1","repo_url":"https://github.com/lizhe00/animatablegaussians","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"nerf","task_name":"NeRF"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}