Abstract
This paper presents a new approach to synthesize face images under different pose changes given a single input image. The approach is based on two observations: 1. a series of face images of a single person under different poses could be mapped to a smooth manifold in the unified feature space. 2. the manifolds from different faces are separated from each other by their dissimilarities. The new manifold estimation is formulated as an energy minimization problem with smoothness constraints. The experiments show that face images under different poses can be robustly synthesized from one input image, even with large pose variations.
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Huang, X., Gao, J., Cheung, Sc.S., Yang, R. (2010). Manifold Estimation in View-Based Feature Space for Face Synthesis across Poses. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12307-8_4
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DOI: https://doi.org/10.1007/978-3-642-12307-8_4
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