发表机构
Monash University; Chongqing University（莫纳什大学; 重庆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高斯头部头像建模中多目标优化权重难以动态调整的问题，提出反事实路线优化框架，通过前瞻评估不同更新路线的效果来动态调制训练目标，在NeRSemble数据集上取得更优性能。
AI 中文摘要
头部头像建模需要联合优化多个目标，这些目标对几何、外观和跨视角一致性具有不同的主导影响。然而，它们的相对有效性在不同训练状态下会发生变化，而现有流程通常依赖固定的损失权重或手工设计的阶段式调度。因此，一个核心挑战是在每个训练状态下识别哪个优化方向更有益。我们提出了一种用于高斯头部头像建模的反事实路线优化框架，该框架从替代更新的实际效果中刻画状态相关的优化偏好，而非预定义的启发式加权。从相同的训练状态出发，我们对几何、外观和联合更新路线执行短视界、受限路线的前瞻，并在统一效用下评估其结果。由此产生的反事实证据被分解为几何-外观偏好和残余联合优势，分别捕捉单个更新方向之间的相对偏好以及协调优化的额外收益。我们进一步将这种离线证据摊销到一个轻量级控制器中，该控制器直接估计当前的优化偏好，并在完整的头像优化过程中对训练目标施加有界调制。在NeRSemble数据集上的实验验证了所提出设计的有效性，在保留更清晰的局部面部结构和更精细细节的同时，持续优于现有方法。
英文摘要
Head avatar modeling requires jointly optimizing multiple objectives with different dominant effects on geometry, appearance, and cross-view consistency. However, their relative effectiveness varies across training states, while existing pipelines typically rely on fixed loss weights or handcrafted stage-wise schedules. A central challenge is therefore to identify which optimization direction is more beneficial at each training state. We propose a counterfactual route optimization framework for Gaussian head avatar modeling, which characterizes state-dependent optimization preference from the realized effects of alternative updates rather than predefined heuristic weighting. Starting from the same training state, we perform short-horizon route-restricted lookahead over geometry, appearance, and joint update routes and evaluate their outcomes under a unified utility. The resulting counterfactual evidence is factorized into a geometry--appearance preference and a residual joint advantage, separately capturing the relative preference between individual update directions and the additional benefit of coordinated optimization. We further amortize this offline evidence into a lightweight controller that directly estimates the current optimization preference and applies bounded modulation to the training objectives during full avatar optimization. Experiments on the NeRSemble dataset validate the effectiveness of the proposed design, consistently outperforming existing methods while preserving clearer local facial structures and finer details.
Comments15 pages, 7 figures, 4 tables
