Learning Spatial-Temporal Coherent Correlations for Speech-Preserving Facial Expression Manipulation
Speech-preserving facial expression manipulation (SPFEM) aims to modify facial emotions while meticulously maintaining the mouth animation associated with spoken content. Current works depend on inaccessible paired training samples for the person, where two aligned frames exhibit the same speech content yet differ in emotional expression, limiting the SPFEM applications in real-world scenarios. In this work, we discover that speakers who convey the same content with different emotions exhibit highly correlated local facial animations in both spatial and temporal spaces, providing valuable supervision for SPFEM. To capitalize on this insight, we propose a novel spatial-temporal coherent co...
| Authors | Tianshui Chen, Jianman Lin, Zhijing Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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