前馈式单样本可驱动纹理网格化身重建
Feed-Forward One-Shot Animatable Textured Mesh Avatar Reconstruction
单图前馈直接输出带纹理贴图的可驱动网格头部，用共享 Transformer 骨干同时回归顶点与纹理图，并用基于重投影的纹理引导把外观锚定到输入图像；对做单图人体/头部重建、以及想把 test-time optimization 换成一次前馈的人，是可复用的架构与监督思路。
原文依据 a feed-forward framework for one-shot animatable mesh head reconstruction that generates high-fidelity, animatable 3D head avatars from a single image
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出 MeshLAM，一个用于单样本可驱动网格头部重建的前馈框架，能够从单张图像生成高保真、可驱动的三维头部化身。与以往依赖耗时的测试时优化或大量多视图数据的工作不同，本方法在单次前向传播中即可从单张图像生成具有内在可驱动性的完整网格表示。我们的方法采用形状与纹理贴图的双分支架构，借助共享 Transformer 骨干提取的图像特征同时处理网格顶点与纹理贴图，从而实现连贯的形状雕刻与外观建模。为防止前馈变形过程中网格塌陷并保证拓扑完整性，我们提出一种基于 GRU 的迭代解码机制，配合渐进式几何变形与纹理细化，并结合一种新颖的基于重投影的纹理引导机制，将外观学习锚定到输入图像上。大量实验表明，本方法在重建质量、驱动能力与计算效率上均优于最优方法。项目主页见 https://meshlam.github.io。
英文原文摘要 1189 字符
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.