Gaussian head avatar modeling improves route based training choices
Counterfactual Route Optimization for Gaussian Head Avatar Modeling
Computer Vision and Pattern Recognition
Summary
Modeling realistic 3D head avatars involves balancing different goals like shape detail, appearance, and consistency from different angles. Instead of using fixed rules for how to train the model, the authors propose a method that tests different training adjustments ahead of time to see which improve results most. They use this information to guide the training process dynamically, improving the final quality of the head avatars with clearer facial details. Their approach works better than previous fixed weighting methods on a standard dataset.
What this means in practice
- •For computer graphics developers: Improve 3D head avatar training by dynamically adjusting optimization priorities for better detail and realism.
- •For virtual reality content creators: Create more realistic avatar faces with clearer local features by using adaptive training methods.
Authors
Shikun Zhang, Yong Li, Yiqun Wang, Qiuhong Ke, Cunjian Chen
Abstract
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.