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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

47,652 subscribers

28,552 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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15,244 Aufrufe • vor 1 Jahr

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Check out 🌟Vid2Sim: Generalizable, Video-based Reconstruction of Appearance, Geometry & Physics for Mesh-Free Simulation #CVPR2025, from Lingjie Liu’s lab at UPenn. Congrats to Chuhao Chen! Vid2Sim aims to achieve system identification by reconstructing geometry, appearance, and physical properties directly from video. It combines learned data priors with closed-loop optimization: a feed-forward predictor trained on physical prior, followed by fast refinement via Neural Jacobian and mesh-free simulation. The system delivers simulation-ready outputs in minutes, with strong generalization across objects and materials. 🏠Project page: #PhysicalAI #AIGC #CV #CG #simulation #graphics

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OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering Contributions: • We propose an occlusion-aware scene division strategy that considers the scene layout and camera co-visibilities. The resulting regions barely contain occlusions, and the corresponding training cameras have a higher average contribution, leading to improved reconstruction results. • We present a region-based rendering technique that accelerates 3D Gaussian splatting in large scenes. It eliminates much of the time-consuming processing of invisible 3D Gaussians, boosting rendering speeds without noticeable quality degradation. • We conduct extensive experiments on several large-scene datasets and demonstrate that OccluGaussian achieves superior rendering quality and faster rendering speed compared to previous state-of-the-art methods.

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