In this paper, we introduce Human-LRM, a single-stage feed-forward Large
Reconstruction Model designed to predict human Neural Radiance Fields (NeRF)
from a single image. Our approach demonstrates remarkable adaptability in
training using extensive datasets containing 3D scans and multi-view capture.
Furthermore, to enhance the model's applicability for in-the-wild scenarios
especially with occlusions, we propose a novel strategy that distills
multi-view reconstruction into single-view via a conditional triplane diffusion
model. This generative extension addresses the inherent variations in human
body shapes when observed from a single view, and makes it possible to
reconstruct the full body human from an occluded image. Through extensive
experiments, we show that Human-LRM surpasses previous methods by a significant
margin on several benchmarks.

利用 Human-LRM 模型，我们可以从一张单一图像中预测人类神经辐射场。通过使用包含 3D 扫描和多视角捕获的大型数据集进行训练，我们的方法展现出了惊人的适应性。此外，为了增强模型在野外场景（尤其是存在遮挡情况下）中的适用性，我们提出了一种将多视角重建转化为单视角的新策略，即条件三平面扩散模型。这个生成式扩展方法解决了从单个视角观察时人体形状的固有变化，并使得从遮挡图像中重建完整的人体成为可能。通过大量实验证明，Human-LRM 在多个基准测试中显著超越了以往的方法。