GHARP：基于大规模重建先验的实时高斯头部动画
GHARP: Real-Time Gaussian Head Animation from Large-Scale Reconstruction Prior
Apple 研究团队提出 GHARP，可从少量输入图像和驱动表情信号实时驱动 3D 人头动画。该方法将问题拆为离线构建身份几何与外观表示、运行时用轻量网络预测表情相关残差两阶段，并加入身体对齐网络消除身体姿态与衣物位置带来的歧义。在 Ava-256 基准上达到 SOTA 质量，在 A100 GPU 上运行速度最高提升 13×，高斯数量减少 8×。
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.
- * Equal Contributors
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来源：Apple Machine Learning Research · machinelearning.apple.com