GHARP
A real-time head animation method based on 3D Gaussian splatting. By decoupling identity representation from expression residual prediction, it achieves high-fidelity real-time facial expression driving from few-view input.
Timeline (last 90 days)
GHARP (Real-time Gaussian Head Animation from Large-scale Reconstruction Prior) adopts a two-stage decoupled architecture: building an identity representation offline and predicting expression residuals at runtime.
The identity stage of GHARP uses large-scale reconstruction priors to initialize or constrain the Gaussian point cloud, enabling the method to remain stable even with very few input views.
GHARP emphasizes that only a few input images are needed, which can lower the data collection barrier, and users may only need a few multi-angle selfies to obtain a 3D head that can be driven in real time.
The paper abstract does not give specific frame rate or hardware performance metrics for GHARP.
The interface between the identity stage and the animation stage of GHARP (the residual representation and the alignment of Gaussian attributes) needs to be carefully designed, otherwise texture drift or geometric distortion can easily occur during expression-driven animation.
Placing heavy computation offline in the cloud and running lightweight animation on the terminal is a feasible hybrid architecture for deploying GHARP-type methods on mobile devices.
All Facts (6)
GHARP (Real-time Gaussian Head Animation from Large-scale Reconstruction Prior) adopts a two-stage decoupled architecture: building an identity representation offline and predicting expression residuals at runtime.
50%UnverifiedThe identity stage of GHARP uses large-scale reconstruction priors to initialize or constrain the Gaussian point cloud, enabling the method to remain stable even with very few input views.
50%UnverifiedGHARP emphasizes that only a few input images are needed, which can lower the data collection barrier, and users may only need a few multi-angle selfies to obtain a 3D head that can be driven in real time.
50%UnverifiedThe paper abstract does not give specific frame rate or hardware performance metrics for GHARP.
50%UnverifiedThe interface between the identity stage and the animation stage of GHARP (the residual representation and the alignment of Gaussian attributes) needs to be carefully designed, otherwise texture drift or geometric distortion can easily occur during expression-driven animation.
50%UnverifiedPlacing heavy computation offline in the cloud and running lightweight animation on the terminal is a feasible hybrid architecture for deploying GHARP-type methods on mobile devices.
50%