{"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/anthronet-conditional-generation-of-humans-1","title":"AnthroNet: Conditional Generation of Humans via Anthropometrics","arxiv_id":"2309.03812","date":"2023-09-07","proceeding":null,"authors":["Francesco Picetti","Shrinath Deshpande","Jonathan Leban","Soroosh Shahtalebi","Jay Patel","Peifeng Jing","Chunpu Wang","Charles Metze III","Cameron Sun","Cera Laidlaw","James Warren","Kathy Huynh","River Page","Jonathan Hogins","Adam Crespi","Sujoy Ganguly","Salehe Erfanian Ebadi"],"abstract":"We present a novel human body model formulated by an extensive set of anthropocentric measurements, which is capable of generating a wide range of human body shapes and poses. The proposed model enables direct modeling of specific human identities through a deep generative architecture, which can produce humans in any arbitrary pose. It is the first of its kind to have been trained end-to-end using only synthetically generated data, which not only provides highly accurate human mesh representations but also allows for precise anthropometry of the body. Moreover, using a highly diverse animation library, we articulated our synthetic humans' body and hands to maximize the diversity of the learnable priors for model training. Our model was trained on a dataset of $100k$ procedurally-generated posed human meshes and their corresponding anthropometric measurements. Our synthetic data generator can be used to generate millions of unique human identities and poses for non-commercial academic research purposes.","url_abs":"https://arxiv.org/abs/2309.03812v1","url_pdf":"https://arxiv.org/pdf/2309.03812v1.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":"anthronet-conditional-generation-of-humans-1","repo_url":"https://github.com/Unity-Technologies/AnthroNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"anthronet-conditional-generation-of-humans-1","repo_url":"https://github.com/Unity-Technologies/com.unity.cv.synthetichumans","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"3d-human-reconstruction","task_name":"3D Human Reconstruction"},{"task_slug":"3d-human-shape-estimation","task_name":"3D Human Shape Estimation"},{"task_slug":"3d-human-pose-and-shape-estimation","task_name":"3D human pose and shape estimation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"human-body-volume-estimation","task_name":"Human Body Volume Estimation"},{"task_slug":"synthetic-data-generation","task_name":"Synthetic Data Generation"}],"methods":[],"datasets_introduced":[{"slug":"unity-synthetic-humans","name":"Unity Synthetic Humans","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}