AnaConDaR: anatomically-constrained data-adaptive facial retargeting
| dc.contributor.author | Wagner, Nicolas | |
| dc.contributor.author | Schwanecke, Ulrich | |
| dc.contributor.author | Botsch, Mario | |
| dc.date.accessioned | 2025-11-14T10:27:08Z | |
| dc.date.available | 2025-11-14T10:27:08Z | |
| dc.date.issued | 2024-06-27 | |
| dc.description.abstract | Offline facial retargeting, i.e., transferring facial expressions from a source to a target character, is a common production task that still regularly leads to considerable algorithmic challenges. This task can be roughly dissected into the transfer of sequential facial animations and non-sequential blendshape personalization. Both problems are typically solved by data-driven methods that require an extensive corpus of costly target examples. Other than that, geometrically motivated approaches do not require intensive data collection but cannot account for character-specific deformations and are known to cause manifold visual artifacts. We present AnaConDaR, a novel method for offline facial retargeting, as a hybrid of data-driven and geometry-driven methods that incorporates anatomical constraints through a physics-based simulation. As a result, our approach combines the advantages of both paradigms while balancing out the respective disadvantages. In contrast to other recent concepts, AnaConDaR achieves substantially individualized results even when only a handful of target examples are available. At the same time, we do not make the common assumption that for each target example a matching source expression must be known. Instead, AnaConDaR establishes correspondences between the source and the target character by a data-driven embedding of the target examples in the source domain. We evaluate our offline facial retargeting algorithm visually, quantitatively, and in two user studies. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/44192 | |
| dc.language.iso | en | |
| dc.relation.ispartofseries | Computers & graphics : CAG; 122 | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Facial animation | en |
| dc.subject | Offline performance retargeting | en |
| dc.subject | Physics-based simulation | en |
| dc.subject.ddc | 004 | |
| dc.title | AnaConDaR: anatomically-constrained data-adaptive facial retargeting | en |
| dc.type | Text | |
| dc.type.publicationtype | Article | |
| dcterms.accessRights | open access | |
| eldorado.doi.register | false | |
| eldorado.secondarypublication | true | |
| eldorado.secondarypublication.primarycitation | Nicolas Wagner, Ulrich Schwanecke, Mario Botsch, AnaConDaR: Anatomically-Constrained Data-Adaptive Facial Retargeting, Computers & Graphics, Volume 122, 2024, 103988, https://doi.org/10.1016/j.cag.2024.103988. | |
| eldorado.secondarypublication.primaryidentifier | https://doi.org/10.1016/j.cag.2024.103988 |