Photorealistic Dynamic Hair Reconstruction with Strand Gaussians

Reconstructing dynamic human hair from multi-view videos is essential for creating high-quality 3D assets for digital avatars and animation. Existing methods augment static hair reconstruction with learned hair dynamics, facing challenges in balancing frame-wise photometric fidelity and optimization efficiency. We present a Strand Gaussian based framework for reconstructing time-varying hair geometry while maintaining photometric fidelity for the observed sequence and preserving photorealistic appearance. Qualitative and quantitative evaluations on the public NeRSemble dataset suggests that our method achieves reconstruction quality that outperforms state-of-the-art methods by a significant margin.