GaussianStyle：通过StyleGAN实现的高斯头部虚拟化身
GaussianStyle: Gaussian Head Avatar via StyleGAN
AI总结：
提出GaussianStyle框架，融合3DGS与StyleGAN，实现高保真动态头部建模，在重演、新视角合成和动画任务上达到最先进性能。
摘要：
现有方法如神经辐射场（NeRF）和三维高斯泼溅（3DGS）在面部属性控制（如面部动画和组件编辑）方面取得了显著进展，但在动态头部建模中仍面临细粒度表示和可扩展性的挑战。为解决这些局限，我们提出了GaussianStyle，一个新颖的框架，将3DGS的体积优势与StyleGAN的强大隐式表示相结合。GaussianStyle利用高斯点保留表情和姿态等结构信息，同时将隐式体积表示投影到StyleGAN中，以捕获高频细节并缓解神经纹理渲染中常见的过度平滑问题。实验结果表明，我们的方法在重演、新视角合成和动画方面达到了最先进的性能。
英文摘要：
Existing methods like Neural Radiation Fields (NeRF) and 3D Gaussian Splatting (3DGS) have made significant strides in facial attribute control such as facial animation and components editing, yet they struggle with fine-grained representation and scalability in dynamic head modeling. To address these limitations, we propose GaussianStyle, a novel framework that integrates the volumetric strengths of 3DGS with the powerful implicit representation of StyleGAN. The GaussianStyle preserves structural information, such as expressions and poses, using Gaussian points, while projecting the implicit volumetric representation into StyleGAN to capture high-frequency details and mitigate the over-smoothing commonly observed in neural texture rendering. Experimental outcomes indicate that our method achieves state-of-the-art performance in reenactment, novel view synthesis, and animation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
