MeshLAM：基于前馈的单次可动画纹理网格人像重建
MeshLAM: Feed-Forward One-Shot Animatable Textured Mesh Avatar Reconstruction
AI总结：
MeshLAM通过单次前馈过程从单张图像生成高保真、可动画的3D人脸网格人像，采用双形状和纹理映射架构及改进的纹理指导机制，提升重建质量与动画能力。
摘要：
我们介绍了MeshLAM，一种基于前馈的单次可动画网格头重建框架，能够从单张图像生成高保真的可动画3D人脸人像。与依赖耗时测试时优化或大量多视角数据的先前工作不同，我们的方法在单次前向传递中从单张图像生成完整的网格表示，并具备内在的可动画性。我们的方法采用双形状和纹理映射架构，同时处理网格顶点和纹理映射，利用共享的Transformer主干提取的图像特征。为防止网格塌陷并确保拓扑完整性，在前馈变形过程中提出了一种基于迭代GRU的解码机制，结合逐步的几何变形和纹理细化，并辅以新颖的投影基于纹理指导机制，将外观学习锚定在输入图像上。大量实验表明，我们的方法在重建质量、动画能力和计算效率方面均优于现有最先进方法。项目页面：https://meshlam.github.io.
英文摘要：
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.
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。
