Single-View 3D Human Digitalization with Large Reconstruction Models paper page: huggingface.co/papers/2401.1… 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.

Check out our #CVPR2023 paper on 3D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labels youtu.be/vXGPW2nDHZ4 Huge shout out to @JenWeng4 who interned in our team last summer and did all the work!