单图生成可实时动画的3D头像，无需训练或额外网络
LAM: Large Avatar Model for One-shot Animatable Gaussian Head
- 用Transformer融合多尺度图像特征，直接生成可动画的高斯头像
- 单次前向传播完成重建，支持跨平台实时渲染与重演
- 适合需快速生成动画头像的元宇宙、直播等场景
我们提出LAM，一种基于单张图像生成可动画高斯头像的大型虚拟人模型。与以往依赖大量视频训练或推理时需辅助神经网络的方法不同，LAM在一次前向传播中即可生成即刻可动画、可渲染的高斯头像，无需额外网络或后处理。其核心是规范高斯属性生成器，以FLAME规范点为查询，通过Transformer与多尺度图像特征交互，精准预测规范空间中的高斯属性。重建后的规范高斯虚拟人可采用标准线性混合皮肤（LBS）结合校正混合形状进行动画，实现跨平台实时渲染。实验表明，LAM在现有基准上优于当前最佳方法。代码与视频见https://aigc3d.github.io/projects/LAM/
原文摘要 · Abstract (English)
We present LAM, an innovative Large Avatar Model for animatable Gaussian head reconstruction from a single image. Unlike previous methods that require extensive training on captured video sequences or rely on auxiliary neural networks for animation and rendering during inference, our approach generates Gaussian heads that are immediately animatable and renderable. Specifically, LAM creates an animatable Gaussian head in a single forward pass, enabling reenactment and rendering without additional networks or post-processing steps. This capability allows for seamless integration into existing rendering pipelines, ensuring real-time animation and rendering across a wide range of platforms, including mobile phones. The centerpiece of our framework is the canonical Gaussian attributes generator, which utilizes FLAME canonical points as queries. These points interact with multi-scale image features through a Transformer to accurately predict Gaussian attributes in the canonical space. The reconstructed canonical Gaussian avatar can then be animated utilizing standard linear blend skinning (LBS) with corrective blendshapes as the FLAME model did and rendered in real-time on various platforms. Our experimental results demonstrate that LAM outperforms state-of-the-art methods on existing benchmarks. Our code and video are available at https://aigc3d.github.io/projects/LAM/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。