Trainable videorealistic speech animation
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2005
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/8020 |
| _version_ | 1811083355628765184 |
|---|---|
| author | Ezzat, Tony F. (Tony Farid) |
| author2 | Tomaso Poggio. |
| author_facet | Tomaso Poggio. Ezzat, Tony F. (Tony Farid) |
| author_sort | Ezzat, Tony F. (Tony Farid) |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002. |
| first_indexed | 2024-09-23T12:31:45Z |
| format | Thesis |
| id | mit-1721.1/8020 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:31:45Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/80202019-04-12T09:12:36Z Trainable videorealistic speech animation Ezzat, Tony F. (Tony Farid) Tomaso Poggio. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002. Includes bibliographical references (p. 53-58). I describe how to create with machine learning techniques a generative, videorealistic, speech animation module. A human subject is first recorded using a videocamera as he/she utters a pre-determined speech corpus. After processing the corpus automatically, a visual speech module is learned from the data that is capable of synthesizing the human subject's mouth uttering entirely novel utterances that were not recorded in the original video. The synthesized utterance is re-composited onto a background sequence which contains natural head and eye movement. The final output is videorealistic in the sense that it looks like a video camera recording of the subject. At run time, the input to the system can be either real audio sequences or synthetic audio produced by a text-to-speech system, as long as they have been phonetically aligned. The two key contributions of this work are * a variant of the multidimensional morphable model (MMM) [4] [26] [25] to synthesize new, previously unseen mouth configurations from a small set of mouth image prototypes, * a trajectory synthesis technique based on regularization, which is automatically trained from the recorded video corpus, and which is capable of synthesizing trajectories in MMM space corresponding to any desired utterance. Results are presented on a series of numerical and psychophysical experiments designed to evaluate the synthetic animations. by Tony Farid Ezzat. Ph.D. 2005-08-24T22:07:30Z 2005-08-24T22:07:30Z 2002 2002 Thesis http://hdl.handle.net/1721.1/8020 52293159 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 58 p. 3669478 bytes 3669239 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Ezzat, Tony F. (Tony Farid) Trainable videorealistic speech animation |
| title | Trainable videorealistic speech animation |
| title_full | Trainable videorealistic speech animation |
| title_fullStr | Trainable videorealistic speech animation |
| title_full_unstemmed | Trainable videorealistic speech animation |
| title_short | Trainable videorealistic speech animation |
| title_sort | trainable videorealistic speech animation |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/8020 |
| work_keys_str_mv | AT ezzattonyftonyfarid trainablevideorealisticspeechanimation |