Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool, but high-resolution scans are often slow and expensive due to extensive data acquisition requirements. Traditional MRI reconstruction methods aim to expedite this process by filling in …
In medical imaging, the primary challenge is collecting large-scale labeled data due to privacy concerns, logistics, and high labeling costs. In this work, we present the UK Biobank Organs and Bones (UKBOB), the largest labeled dataset of body …
Multimodal Large Language Models (MLLMs) have demonstrated impressive 2D image/video understanding capabilities.However, there are no publicly standardized benchmarks to assess the abilities of MLLMs in understanding the 4D objects.In this paper, we …
We propose a novel zero-shot approach for keypoint detection on 3D shapes. Point-level reasoning on visual data is challenging as it requires precise localization capability, posing problems even for powerful models like DINO or CLIP. Traditional …
The field of computer graphics was revolutionized by models such as Neural Radiance Fields and 3D Gaussian Splatting, displacing triangles as the dominant representation for photogrammetry. In this paper, we argue for a triangle comeback. We develop …
Recent advances in radiance field reconstruction, such as 3D Gaussian Splatting (3DGS), have achieved high-quality novel view synthesis and fast rendering by representing scenes with compositions of Gaussian primitives. However, 3D Gaussians present …
We base our work on 3D Gaussian Splatting (3DGS), a scene representation composed of a mixture of Gaussians. Predicting such mixtures for a human from a single input image is challenging, as it is a non-uniform density (with a many-to-one …
Neural radiance fields (NeRFs) generally require many images with accurate poses for accurate novel view synthesis, which does not reflect realistic setups where views can be sparse and poses can be noisy. Previous solutions for learning NeRFs with …
Advancements in 3D Gaussian Splatting (GS) have significantly accelerated 3D reconstruction and generation. However, it may require a very large number of Gaussians, which can become a substantial memory footprint. This paper introduces GES …
We present `Magic123`, a two-stage coarse-to-fine solution for high-quality, textured 3D meshes generation from a single unposed image in the wild using both 2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a …