Spacetime expression cloning for blendshapes

Author:

Seol Yeongho1,Lewis J.P.2,Seo Jaewoo3,Choi Byungkuk3,Anjyo Ken4,Noh Junyong3

Affiliation:

1. KAIST and Weta Digital

2. Weta Digital

3. KAIST

4. OLM Digital and JST CREST

Abstract

The goal of a practical facial animation retargeting system is to reproduce the character of a source animation on a target face while providing room for additional creative control by the animator. This article presents a novel spacetime facial animation retargeting method for blendshape face models. Our approach starts from the basic principle that the source and target movements should be similar. By interpreting movement as the derivative of position with time, and adding suitable boundary conditions, we formulate the retargeting problem as a Poisson equation. Specified (e.g., neutral) expressions at the beginning and end of the animation as well as any user-specified constraints in the middle of the animation serve as boundary conditions. In addition, a model-specific prior is constructed to represent the plausible expression space of the target face during retargeting. A Bayesian formulation is then employed to produce target animation that is consistent with the source movements while satisfying the prior constraints. Since the preservation of temporal derivatives is the primary goal of the optimization, the retargeted motion preserves the rhythm and character of the source movement and is free of temporal jitter. More importantly, our approach provides spacetime editing for the popular blendshape representation of facial models, exhibiting smooth and controlled propagation of user edits across surrounding frames.

Funder

KOCCA/MCST

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

Cited by 49 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A review of motion retargeting techniques for 3D character facial animation;Computers & Graphics;2024-10

2. Facial Animation Retargeting by Unsupervised Learning of Graph Convolutional Networks;2024 Nicograph International (NicoInt);2024-06-14

3. AU-Aware Dynamic 3D Face Reconstruction from Videos with Transformer;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

4. Learning a crowd-powered perceptual distance metric for facial blendshapes;EURASIP Journal on Image and Video Processing;2023-05-15

5. A Selective Expression Manipulation With Parametric 3D Facial Model;IEEE Access;2023

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