{"id":160946,"date":"2010-09-27T00:00:00","date_gmt":"2010-09-27T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/photo-real-lips-synthesis-with-trajectory-guided-sample-selection\/"},"modified":"2018-10-16T20:54:23","modified_gmt":"2018-10-17T03:54:23","slug":"photo-real-lips-synthesis-with-trajectory-guided-sample-selection","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/photo-real-lips-synthesis-with-trajectory-guided-sample-selection\/","title":{"rendered":"Photo-Real Lips Synthesis with Trajectory-Guided Sample Selection"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper, we propose an HMM trajectory-guided, real image sample concatenation approach to photo-real talking head synthesis. It renders a smooth and natural video of articulators in sync with given speech signals. An audio-visual database is used to train a statistical Hidden Markov Model (HMM) of lips movement first and the trained model is then used to generate a visual parameter trajectory of lips movement for given speech signals, all in the maximum likelihood sense. The HMM generated trajectory is then used as a guide to select, in the original training database, an optimal sequence of mouth images which are then stitched back to a background head video. The whole procedure is fully automatic and data driven. With an audio\/video footage as short as 20 minutes from a speaker, the proposed system can synthesize a highly photo-real video in sync with the given speech signals. This system won the FIRST place in the Audio-Visual match contest in LIPS2009 Challenge, which was perceptually evaluated by recruited human subjects.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we propose an HMM trajectory-guided, real image sample concatenation approach to photo-real talking head synthesis. It renders a smooth and natural video of articulators in sync with given speech signals. An audio-visual database is used to train a statistical Hidden Markov Model (HMM) of lips movement first and the trained model is [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-160946","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"International Speech Communication Association","msr_edition":"Speech Synthesis Workshop 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