@inproceedings{VAREN:2024,
  title = {{VAREN}: Very Accurate and Realistic Equine Network},
  booktitle = {IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)},
  abstract = {Data-driven three-dimensional parametric shape models
  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 animal
  species, not to mention the difficulty of accessing subjects
  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 articulated
  shape model. We introduce VAREN, a novel 3D articulated
  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 between
  articulation and muscle deformation during articulated
  motion.},
  pages = {5374-5383},
  month = jun,
  year = {2024},
  slug = {varen-2024},
  author = {Zuffi, S. and Mellbin, Y. and Li, C. and Hoeschle, M. and Kjellstrom, H and Polikovsky, S. and Hernlund, E. and Black, M. J.},
  url = {https://varen.is.tue.mpg.de/},
  month_numeric = {6}
}