AnthroNet: Conditional Generation of Humans via Anthropometrics
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
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.