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Updated video link.

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Emiliana 2024-06-01 00:17:08 +02:00 • committed by GitHub
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@ -18,7 +18,7 @@ Since the landmarks used by OpenSeeFace are a bit different from those used by o
From general observation, OpenSeeFace performs well in adverse conditions (low light, high noise, low resolution) and keeps tracking faces through a very wide range of head poses with relatively high stability of landmark positions. Compared to MediaPipe, OpenSeeFace landmarks remain more stable in challenging conditions and it accurately represents a wider range of mouth poses. However, tracking of the eye region can be less accurate. From general observation, OpenSeeFace performs well in adverse conditions (low light, high noise, low resolution) and keeps tracking faces through a very wide range of head poses with relatively high stability of landmark positions. Compared to MediaPipe, OpenSeeFace landmarks remain more stable in challenging conditions and it accurately represents a wider range of mouth poses. However, tracking of the eye region can be less accurate.
I ran OpenSeeFace on a sample clip from the video presentation for [3D Face Reconstruction with Dense Landmarks](https://microsoft.github.io/DenseLandmarks/) by Wood et al. to compare it to MediaPipe and their approach. You can watch the result [here](https://cdn.discordapp.com/attachments/720652345540870237/1016320201807106118/OSFMediaPipe3DFR.mp4). I ran OpenSeeFace on a sample clip from the video presentation for [3D Face Reconstruction with Dense Landmarks](https://microsoft.github.io/DenseLandmarks/) by Wood et al. to compare it to MediaPipe and their approach. You can watch the result [here](https://www.vseeface.icu/assets/media/OSFMediaPipe3DFR.mp4).
# Usage # Usage