Clustering of the Blendshape Facial Model

Stevo Racković, Cláudia Soares, Dušan Jakovetić, Zoranka Desnica, Relja Ljubobratović

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Digital human animation relies on high-quality 3D models of the human face-rigs. A face rig must be accurate and, at the same time, fast to compute. One of the most common rigging models is the blendshape model. We present a novel approach for learning the inverse rig parameters at increased accuracy and decreased computational cost at the same time. It is based on a two fold clustering of the blendshape face model. Our method focuses exclusively on the underlying space of deformation and produces clusters in both the mesh space and the controller space-something that was not investigated in previous literature. This segmentation finds intuitive and meaningful connections between groups of vertices on the face and deformation controls, and further these segments can be observed independently. A separate model for solving the inverse rig problem is then learnt for each segment. Our method is completely unsupervised and highly parallelizable.

Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)978-9-0827-9706-0
ISBN (Print)978-1-6654-0900-1
Publication statusPublished - 2021
Event29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference29th European Signal Processing Conference, EUSIPCO 2021


  • Blendshape
  • Gaussian process regression
  • Inverse rig
  • K-means
  • Point cloud clustering


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