PanoLAM：基于单张无姿态图像进行高斯全头合成的大型虚拟人模型
PanoLAM: Large Avatar Model for Gaussian Full-Head Synthesis from One-shot Unposed Image
AI总结：
提出PanoLAM框架，通过双分支结构和从粗到细的流水线，利用合成数据训练，实现从单张无姿态图像快速前馈重建高保真高斯全头模型。
摘要：
我们提出了一种前馈框架，用于从单张无姿态图像进行高斯全头合成。与以往依赖耗时的GAN反演和测试时优化的工作不同，我们的框架能在单次前向传播中，根据单张无姿态图像重建高斯全头模型。这使得推理期间能够实现快速重建和渲染。为了缓解大规模3D头部资产的缺乏，我们提出了一个源自已训练3D GAN的大规模合成数据集，并仅使用合成数据来训练我们的框架。为了实现高效的高保真生成，我们引入了从粗到细的高斯头部生成流水线，其中来自FLAME模型的稀疏点通过transformer块与图像特征交互，以进行特征提取和粗略形状重建，随后对其进行致密化以实现高保真重建。为了充分利用预训练3D GAN中的先验知识以实现有效重建，我们提出了一个双分支框架，该框架有效地聚合了结构化球面三平面特征和非结构化点云特征，以实现更有效的高斯头部重建。实验结果证明了我们的框架相对于现有工作的有效性。项目页面位于：https://panolam.github.io/。
英文摘要：
We present a feed-forward framework for Gaussian full-head synthesis from a single unposed image. Unlike previous work that relies on time-consuming GAN inversion and test-time optimization, our framework can reconstruct the Gaussian full-head model given a single unposed image in a single forward pass. This enables fast reconstruction and rendering during inference. To mitigate the lack of large-scale 3D head assets, we propose a large-scale synthetic dataset from trained 3D GANs and train our framework using only synthetic data. For efficient high-fidelity generation, we introduce a coarse-to-fine Gaussian head generation pipeline, where sparse points from the FLAME model interact with the image features by transformer blocks for feature extraction and coarse shape reconstruction, which are then densified for high-fidelity reconstruction. To fully leverage the prior knowledge residing in pretrained 3D GANs for effective reconstruction, we propose a dual-branch framework that effectively aggregates the structured spherical triplane feature and unstructured point-based features for more effective Gaussian head reconstruction. Experimental results show the effectiveness of our framework towards existing work. Project page at: https://panolam.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。
