单张图生成可动画高精度3D人脸，无需优化
MeshLAM: Feed-Forward One-Shot Animatable Textured Mesh Avatar Reconstruction
- 共享Transformer骨干提取特征，双路处理形状与纹理
- 单次前向传播完成网格重建，误差仅1.78mm
- 适合实时虚拟角色生成，对算力要求低
我们提出MeshLAM，一种前向传播的单张图像可动画化网格头像重建框架，能从单张图像生成高保真、可动画的3D头像。不同于依赖耗时测试优化或多视角数据的方法，本方法在一次前向传播中生成完整网格表示，并具备内在可动画性。采用双形状-纹理图架构，通过共享Transformer骨干提取的图像特征同步处理网格顶点与纹理图，实现一致的形体雕刻与外观建模。为防止前向变形中的网格坍缩并确保拓扑完整性，提出基于迭代GRU的解码机制，实现渐进式几何变形与纹理精炼，并引入基于重投影的纹理引导机制，将外观学习锚定于输入图像。大量实验表明，本方法在重建质量、动画能力与计算效率上均优于现有最佳方法。
原文摘要 · Abstract (English)
We introduce MeshLAM, a feed-forward framework for one-shot animatable mesh head reconstruction that generates high-fidelity, animatable 3D head avatars from a single image. Unlike previous work that relies on time-consuming test-time optimization or extensive multi-view data, our method produces complete mesh representations with inherent animatability from a single image in a single forward pass. Our approach employs a dual shape and texture map architecture that simultaneously processes mesh vertices and texture map with extracted image features from a shared transformer backbone, allowing for coherent shape carving and appearance modeling. To prevent mesh collapse and ensure topological integrity during feed-forward deformation, we propose an iterative GRU-based decoding mechanism with progressive geometry deformation and texture refinement, coupled with a novel reprojection-based texture guidance mechanism that anchors appearance learning to the input image. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in reconstruction quality, animation capability, and computational efficiency. Project page at https://meshlam.github.io.
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