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.
|Title of host publication||2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - 2018 Aug 29|
|Event||25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece|
Duration: 2018 Oct 7 → 2018 Oct 10
|Name||Proceedings - International Conference on Image Processing, ICIP|
|Conference||25th IEEE International Conference on Image Processing, ICIP 2018|
|Period||18/10/7 → 18/10/10|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2016R1A2B2014525)
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Signal Processing