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We utilize the same rig framework for both tracking and animation to remove the difficulties associated with retargeting the semantics of one framework to another. Our carefully designed set of Simon\u2010Says expressions and regularizers is used to calibrate each rig to the motion signatures of the relevant performer or target. Although a uniform set of Simon\u2010Says expressions can likely be used for all person\u2010to\u2010person retargeting, we argue that person\u2010to\u2010virtual\u2010character retargeting benefits from an expression set that captures the distinct motion signature of the virtual character rig. The Simon\u2010Says calibrated rigs tend to produce the desired expressions when exercising animation controls. Unfortunately, these well\u2010calibrated rigs still lead to undesirable controls when tracking a performance, even though they generally produce acceptable geometry reconstructions. Thus, we propose a fine\u2010tuning approach that modifies the rig used by the tracker to promote the output of more semantically meaningful animation controls, facilitating high efficacy retargeting. To better address real\u2010world scenarios, the fine\u2010tuning relies on implicit differentiation so that the tracker can be treated as a potentially non\u2010differentiable black box. Experiments demonstrate the benefits of our calibration methods on high\u2010fidelity expressive performance retargeting for different capture conditions, trackers, and rig frameworks.<\/jats:p>","DOI":"10.1111\/cgf.70417","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T10:20:18Z","timestamp":1776248418000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving Facial Rig Semantics for Tracking and Retargeting"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3986-6374","authenticated-orcid":false,"given":"D.","family":"Omens","sequence":"first","affiliation":[{"name":"Stanford University  USA"},{"name":"Epic Games  USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Thurman","sequence":"additional","affiliation":[{"name":"Stanford University  USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4958-502X","authenticated-orcid":false,"given":"J.","family":"Yu","sequence":"additional","affiliation":[{"name":"Epic Games  USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6853-2499","authenticated-orcid":false,"given":"R.","family":"Fedkiw","sequence":"additional","affiliation":[{"name":"Stanford University  USA"},{"name":"Epic Games  USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"e_1_2_12_2_2","doi-asserted-by":"crossref","unstructured":"AnejaD. 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