Counterfactual Route Optimization for Gaussian Head Avatar Modeling

2026-10-07

AI Summary

This study addresses the challenge that fixed weights struggle to adapt to evolving training states in Gaussian head avatar modeling. To overcome this limitation, we propose a counterfactual path optimization framework. This method evaluates the effects of geometry and appearance updates via short-horizon lookahead, generating a lightweight controller to dynamically modulate target weights. By relying on actual update feedback rather than heuristic rules, it identifies state-dependent preferences and effectively decouples geometry-appearance trade-offs. The approach integrates 3D Gaussian Splatting, counterfactual reasoning, and reinforcement learning-style lookahead search. Extensive experiments on the NeRSemble dataset demonstrate that our framework outperforms existing methods, significantly enhancing local facial structures and fine detail clarity.

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.

Problem

• Head Avatar Modeling
• Multi-objective Optimization
• Loss Weighting
• Training Dynamics
• Gaussian Head Avatar

Innovation

• Counterfactual Route Optimization
• Gaussian Head Avatar
• Lookahead
• Amortized Controller
• Dynamic Loss Modulation