CVPR2025
GASP: Gaussian Avatars with Synthetic Priors
Jack R. Saunders, Charlie Hewitt, Yanan Jian, Marek Kowalski, Tadas Baltrusaitis, Yiye Chen, Darren Cosker, Virginia Estellers, Nicholas Gyde, Vinay P. Namboodiri, Benjamin E. Lundell
摘要
Figure 1 . We propose GASP, a novel model for creating photorealistic, real-time, animatable, 360 • avatars from easily-captured data. We train a generative prior model of Gaussian Avatars on Synthetic data. The prior allows our model to be fit using a single image or a short video with the prior accounting for the unseen views. This lets users create their avatar with only a webcam or smartphone.