Fitting Facial Models to Spatial Points: Blendshape Approaches and Benchmark

Taelim Choi, Jiwoo Kang, Hyewon Song, Sanghoon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Blendshape is one of the most common facial representation used for 3D animation, 3D game and virtual reality. In this paper, four representative blendshape approaches are benchmarked: global, delta, mean-delta, and SVD-based blend-shapes. When fitting the blendshape models to sparse facial points, the obtained facial shape highly depends on fitting approach due to the lack of the fitted points. Therefore, it is important to set up appropriate criteria for comparing and verifying the performance of the approaches. In this paper, we use four kinds of metrics that are utilized to measure the performance of the approaches: fitting, landmark, and vertex errors and coefficient sparsity. Through the experimental results, it is verified that the benchmarks are very effective to measure the subjective quality of blendshape.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages2650-2654
Number of pages5
ISBN (Electronic)9781479970612
DOIs
Publication statusPublished - 2018 Aug 29
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 2018 Oct 72018 Oct 10

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
CountryGreece
CityAthens
Period18/10/718/10/10

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Choi, T., Kang, J., Song, H., & Lee, S. (2018). Fitting Facial Models to Spatial Points: Blendshape Approaches and Benchmark. In 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings (pp. 2650-2654). [8451448] (Proceedings - International Conference on Image Processing, ICIP). IEEE Computer Society. https://doi.org/10.1109/ICIP.2018.8451448