The field of 3D human modeling and medical imaging is rapidly advancing, with a focus on developing more accurate and efficient methods for reconstructing human bodies and analyzing medical images. Recent research has explored the use of neural networks and machine learning algorithms to improve the accuracy and speed of 3D reconstruction, as well as the development of new methods for modeling and analyzing human motion. In medical imaging, researchers are working to improve the resolution and accuracy of imaging modalities such as ultrasound computed tomography (USCT), and to develop new methods for analyzing and interpreting medical images. Notably, the use of generative neural physics frameworks and learned optimizers is showing promise in advancing the field.

Some noteworthy papers in this area include: L-SR1, which proposes a novel learned second-order optimizer that introduces a trainable preconditioning unit to enhance the classical Symmetric-Rank-One algorithm. Learnable SMPLify, a neural framework that replaces the iterative fitting process in SMPLify with a single-pass regression model, achieving nearly 200x faster runtime compared to SMPLify. GaussianArt, which introduces a unified representation that jointly models geometry and motion using articulated 3D Gaussians, improving robustness in motion decomposition and supporting articulated objects with up to 20 parts. Snap-Snap, a method that can reconstruct the entire human in 190 ms on a single NVIDIA RTX 4090, with two images at a resolution of 1024x1024, demonstrating state-of-the-art performance on the THuman2.0 and cross-domain datasets. ATLAS, a high-fidelity body model learned from 600k high-resolution scans, which explicitly decouples the shape and skeleton bases, enabling enhanced shape expressivity and fine-grained customization of body attributes.