Image analysis and graphics synthesis can be achieved with learning techniques using directly image examples without physically-based, 3D models. In our technique: -- the mapping from novel images to a vector of "pose" and "expression" parameters can be learned from a small set of example images using a function approximation technique that we call an analysis network; -- the inverse mapping from input "pose" and "expression" parameters to output images can be synthesized from a small set of example images and used to produce new images using a similar synthesis network. The techniques described here have several applications in computer graphics, special effects, interactive multimedia and very low bandwidth teleconferencing.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/7214 |
| Date | 01 November 1993 |
| Creators | Beymer, David, Shashua, Amnon, Poggio, Tomaso |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 21 p., 979413 bytes, 1030628 bytes, application/octet-stream, application/pdf |
| Relation | AIM-1431, CBCL-080 |
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