Sparse coding for flexible, robust 3D facial-expression synthesis.
Synthesizing realistic facial expressions from photographs - Semantic Scholar
We present new techniques for creating photorealistic textured 3D facial models from photographs of a human subject, and for creating smooth transitions between different facial expressions by morphing between these different models. Starting from several uncalibrated views of a human subject, we employ a user-assisted technique to recover the camera poses corresponding to the views as well as the 3D coordinates of a sparse set of chosen locations on the subject's face. A scattered data interpolation technique is then used to deform a generic face mesh to fit the particular geometry of the subject's face. Having recovered the camera poses and the facial geometry, we extract from the input images one or more texture maps for the model. This process is repeated for several facial expressions of a particular subject.
Synthesizing realistic facial expressions from photographs (1998)
Scientific Research An Academic Publisher. Affiliation s. In our daily life, facial information is important to enrich speech communication. The face-to-face communication can give us not only linguistic information but also the facial identity and expressions, which sometimes plays an essential role to make a person be relieved, attracted, or affected. The same thing can be said in human-computer interaction.
Skip to search form Skip to main content. Starting from several uncalibrated views of a human subject, we employ a user-assisted technique to recover the camera poses corresponding to the views as well as the 3D coordinates of a sparse set of chosen locations on the subject's face. View on ACM. Save to Library.