Towards Unified Dynamic Face Landmark Detection
Although advancements in face landmark detection (FLD) methods continue to push performance boundaries, they overlook two major functional limitations: (1) different network parameters need to be trained independently for each ``$N$-point'' benchmark dataset, and (2) a model trained on an ``$N$-point'' dataset reliably outputs only the $N$ landmarks. In our work, we first conceptualize Face Part-Anchored Landmark Positions (FPALPs), wherein each landmark is treated as a progression value between zero (start) and one (end) along a face part's contour. Every landmark can be expressed in the FPALP format, irrespective of its source dataset, hence unlocking the ability to unify all ``$N$-poin...
| Authors | Sebastian Regalado, Varshanth R. Rao, Ruowei Jiang et al. |
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| Primary category | cs.CV |
| Published | 11 August 2026 |
| Last updated at source | 24 September 2026 |
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