GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar Editor

2025-01-17

AI Summary

This work addresses critical challenges in text-driven 4D Gaussian avatar editing—namely, severe motion occlusion, spatiotemporal inconsistency, and photometric distortion. Methodologically: (1) We propose a Weighted Alpha Blending Equation (WABE) to dynamically model geometric and visibility changes during motion, effectively mitigating occlusion artifacts; (2) we design a conditional Generative Adversarial Network (cGAN) to jointly optimize photorealism and 4D spatiotemporal consistency; (3) we integrate 3D Gaussian splatting, differentiable rendering, and a dynamic visibility-weighted fusion mechanism. Experiments demonstrate that our framework significantly outperforms existing methods on multi-subject editing tasks. The generated avatars exhibit fine-grained controllability over expressions, poses, and viewpoints; strong inter-frame temporal coherence; multi-view consistency; and high photorealism.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

We introduce GaussianAvatar-Editor, an innovative framework for text-driven editing of animatable Gaussian head avatars that can be fully controlled in expression, pose, and viewpoint. Unlike static 3D Gaussian editing, editing animatable 4D Gaussian avatars presents challenges related to motion occlusion and spatial-temporal inconsistency. To address these issues, we propose the Weighted Alpha Blending Equation (WABE). This function enhances the blending weight of visible Gaussians while suppressing the influence on non-visible Gaussians, effectively handling motion occlusion during editing. Furthermore, to improve editing quality and ensure 4D consistency, we incorporate conditional adversarial learning into the editing process. This strategy helps to refine the edited results and maintain consistency throughout the animation. By integrating these methods, our GaussianAvatar-Editor achieves photorealistic and consistent results in animatable 4D Gaussian editing. We conduct comprehensive experiments across various subjects to validate the effectiveness of our proposed techniques, which demonstrates the superiority of our approach over existing methods. More results and code are available at: [Project Link](https://xiangyueliu.github.io/GaussianAvatar-Editor/).

Problem

• 4D Gaussian Avatars
• Object Occlusion
• Temporal-Spatial Mismatch

Innovation

• 4D Gaussian Avatars
• WABE formula
• conditional adversarial learning