前馈高斯注册用于头像创建与编辑
Feed-forward Gaussian Registration for Head Avatar Creation and Editing
AI总结：
MATCH方法通过高效高斯注册实现高质量头像创建，无需预处理，速度快且支持跨主体应用，优于现有方法。
摘要：
我们提出了MATCH（多视角头像从拓扑对应头部生成），一种多视角高斯注册方法，用于高质量头像创建与编辑。最先进的多视角头像方法需要耗时的头部跟踪和昂贵的头像优化，通常导致总创建时间超过一天。MATCH相反，直接在校准的多视角图像中预测高斯溅射纹理，每帧仅需0.5秒，无需数据预处理。学习的跨帧内在对应关系使快速创建个性化头像，而跨主体对应关系支持表情转移、无优化跟踪、语义编辑和身份插值。我们通过基于变换器的模型建立这些对应关系，该模型在模板网格的固定UV布局中预测高斯溅射纹理。为此，我们引入了新颖的注册引导注意力块，其中每个UV图令牌仅关注描绘其对应网格区域的图像令牌。这种设计相比密集跨视角注意力更高效。MATCH在新视角合成、几何注册和头像生成中优于现有方法，同时使头像创建速度比最接近的竞争对手基线快10倍。代码和模型权重可在项目网站上获取。
英文摘要：
We present MATCH (Multi-view Avatars from Topologically Corresponding Heads), a multi-view Gaussian registration method for high-quality head avatar creation and editing. State-of-the-art multi-view head avatar methods require time-consuming head tracking followed by expensive avatar optimization, often resulting in a total creation time of more than one day. MATCH, in contrast, directly predicts Gaussian splat textures in correspondence from calibrated multi-view images in just 0.5 seconds per frame, without requiring data preprocessing. The learned intra-subject correspondence across frames enables fast creation of personalized head avatars, while correspondence across subjects supports applications such as expression transfer, optimization-free tracking, semantic editing, and identity interpolation. We establish these correspondences end-to-end using a transformer-based model that predicts Gaussian splat textures in the fixed UV layout of a template mesh. To achieve this, we introduce a novel registration-guided attention block, where each UV-map token attends exclusively to image tokens depicting its corresponding mesh region. This design improves efficiency and performance compared to dense cross-view attention. MATCH outperforms existing methods in novel-view synthesis, geometry registration, and head avatar generation, while making avatar creation 10 times faster than the closest competing baseline. The code and model weights are available on the project website.
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