Paper ID: 2309.03812

AnthroNet: Conditional Generation of Humans via Anthropometrics

Francesco Picetti, Shrinath Deshpande, Jonathan Leban, Soroosh Shahtalebi, Jay Patel, Peifeng Jing, Chunpu Wang, Charles Metze, Cameron Sun, Cera Laidlaw, James Warren, Kathy Huynh, River Page, Jonathan Hogins, Adam Crespi, Sujoy Ganguly, Salehe Erfanian Ebadi

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.

Submitted: Sep 7, 2023