GHARP实现面向移动端的实时高斯头部动画
GHARP是一种利用少量人物图像和驱动表情信号实时生成3D头部动画的方法。作者表示，该方法将离线身份重建与运行时动画分离，其中运行时采用面向移动设备优化的轻量级网络，并在预训练重建模型语义结构化的潜空间中预测与表情相关的残差。报告指出，模糊的身体外观可能造成画面模糊和时间闪烁，作者通过身体对齐网络加以处理。作者称该方法在Ava-256基准上达到领先质量，在A100 GPU上最高快13倍，并使用少8倍的高斯点；现有证据未对这些结果进行独立验证。
据报告所述，两阶段设计可让成本较高的身份重建离线完成，同时以面向移动设备优化的轻量级网络执行动画，从而改善运行时与质量之间的权衡。性能和质量数据均由作者报告，现有证据未作独立验证。
research area Computer Visionconference Asian Conference on Computer Vision (ACCV)
content type paperpublished October 2026
GHARP: Real-Time Gaussian Head Animation from Large-Scale Reconstruction Prior
AuthorsAli Benlalah*, Sepehr Johari*, Patricia Vitoria*, Armin Kappeler*, Artem Sevastopolsky, Alexander Jung, Gabriele Fanelli, Kevin Mader, Manuel Breitenstein, Claudia Plüss, Jan Rüegg, Simon Biland, Thomas Etterlin, Dmitry Kostiaev, Mathias Deschler, Brian Amberg, Sebastian Martin
We present GHARP (Real-time Gaussian Head Animation from Large-scale Reconstruction Prior), a method that animates 3D human heads in real time from a few input images of a subject and a driving expression signal. We decouple the problem into an identity stage that builds a representation of the subject’s geometry and appearance offline, and an animation stage that predicts expression-dependent residuals on top of it at runtime. This separation offers a favorable trade-off with respect to fidelity, quality and runtime: the identity stage can be expensive while the animation stage runs a lightweight network, optimized for mobile devices. Our method performs animation in a semantically structured latent space of a pretrained reconstruction model, where expression changes remain spatially contained, making residual prediction efficient. This reconstruction prior provides a consistent spatial layout, allowing fusion of multiple input views into a compact, fixed-size canonical Gaussian representation. While this two-stage design improves the runtime-quality trade-off, it still inherits a problem common to all expression-driven avatar methods: expression codes describe only the face and thus omit body pose and clothing position, making these regions underspecified in the input. The animation network faces an ill-posed mapping and resorts to averaging over conflicting body appearances, producing blur and temporal flicker. We address this with a body alignment network that learns to align the person’s body in the target image with the input reference images, removing the ambiguity from the training signal. Our method achieves state-of-the-art quality on the Ava-256 benchmark while running up to 13× faster on an A100 GPU with 8× fewer Gaussians.
#GHARP#Ava-256#NVIDIA A100#3D Gaussian Splatting#Apple Machine Learning Research