Limitations: Our model-agency training dataset (Sec. 3.2)
is not representative of the entire human population and this
limits SHAPY’s ability to predict larger body shapes. To
address this, we need to find images of more diverse bodies
together with anthropometric measurements and linguistic
shape attributes describing them.
Social impact: Knowing the 3D shape of a person has ad-
vantages, for example, in the clothing industry to avoid un-
necessary returns. If used without consent, 3D shape esti-
mation may invade individuals’ privacy. As with all other
3D pose and shape estimation methods, surveillance and
deep-fake creation is another important risk. Consequently,
SHAPY’s license prohibits such uses.
Acknowledgments: This work was supported by the Max
Planck ETH Center for Learning Systems and the Inter-
national Max Planck Research School for Intelligent Sys-
tems. We thank Tsvetelina Alexiadis, Galina Henz, Clau-
dia Gallatz, and Taylor McConnell for the data collec-
Accurate 3D Body Shape Regression using Metric and Semantic Attributes