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📢 SHeaP: Self-Supervised Head Predictor Learned via 2D Gaussians 📢 Given a single input image, we predict accurate 3D head geometry, pose, and expression. Previous works (e.g. DECA, EMOCA) use differentiable mesh rasterization to learn a self-supervised head geometry predictor via a photometric reconstruction loss. We borrow these ideas,... show more

Matthias Niessner

50,397 subscribers

28,619 Aufrufe • vor 1 Jahr •via X (Twitter)

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4 Kommentare

Profilbild von Felix Taubner

Felix Taubnervor 1 Jahr

Always happy to see new work face trackers!

Profilbild von Rainmaker

Rainmakervor 2 Jahren

Join me as I put several Machine Learning models head-to-head to see which one can beat the market and deliver strong returns. In this free Substack post I share several models that deliver better returns with much lower drawdown compared to Buy-and-Hold approach.

Profilbild von Michael Black

Michael Blackvor 1 Jahr

Nice. I’ve been wanting to replace the old photometric loss with splatting. Results look great.

Profilbild von Karl Mehta

Karl Mehtavor 1 Jahr

A fascinating step forward in precision and training efficiency.

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