Cross-Species 3D Face Morphing via Alignment-Aware Controller
Keywords:Computer Vision (CV)
AbstractWe address cross-species 3D face morphing (i.e., 3D face morphing from human to animal), a novel problem with promising applications in social media and movie industry. It remains challenging how to preserve target structural information and source ﬁne-grained facial details simultaneously. To this end, we propose an Alignment-aware 3D Face Morphing (AFM) framework, which builds semantic-adaptive correspondence between source and target faces across species, via an alignment-aware controller mesh (Explicit Controller, EC) with explicit source/target mesh binding. Based on EC, we introduce Controller-Based Mapping (CBM), which builds semantic consistency between source and target faces according to the semantic importance of different face regions. Additionally, an inference-stage coarse-to-ﬁne strategy is exploited to produce ﬁne-grained meshes with rich facial details from rough meshes. Extensive experimental results in multiple people and animals demonstrate that our method produces high-quality deformation results.
How to Cite
Yan, X., Yu, Z., Ni, B., & Wang, H. (2022). Cross-Species 3D Face Morphing via Alignment-Aware Controller. Proceedings of the AAAI Conference on Artificial Intelligence, 36(3), 3018-3026. https://doi.org/10.1609/aaai.v36i3.20208
AAAI Technical Track on Computer Vision III