VAREN: Very Accurate and Realistic Equine Network

Silvia Zuffi, Ylva Mellbin, Ci Li, Markus Höschle, Hedvig Kjellström, Senya Polikovsky, Elin Hernlund, Michael J. Black

Abstract: Data-driven three-dimensional parametric shape mod-els of the human body have gained enormous popularity both for the analysis of visual data and for the generation of synthetic humans. Following a similar approach for animals does not scale to the multitude of existing ani-mal species, not to mention the difficulty of accessing sub-jects to scan in 3D. However, we argue that for domestic species of great importance, like the horse, it is a highly valuable investment to put effort into gathering a large dataset of real 3D scans, and learn a realistic 3D articu-lated shape model. We introduce VAREN, a novel 3D ar-ticulated parametric shape model learned from 3D scans of many real horses. VAREN bridges synthesis and analysis tasks, as the generated model instances have unprecedented realism, while being able to represent horses of different sizes and shapes. Differently from previous body models, VAREN has two resolutions, an anatomical skeleton, and interpretable, learned pose-dependent deformations, which are related to the body muscles. We show with experiments that this formulation has superior performance with respect to previous strategies for modeling pose-dependent deformations in the human body case, while also being more compact and allowing an analysis of the relationship be-tween articulation and muscle deformation during articu-lated motion. The VAREN model and data are available at https://varen.is.tue.mpg.de.