Feed-forward 3D generative models like the Large Reconstruction Model (LRM)[18] have
demonstrated exceptional generation speed. However, the transformer-based methods do …

Abstract We introduce the Splatter Image an ultra-efficient approach for monocular 3D object
reconstruction. Splatter Image is based on Gaussian Splatting which allows fast and high …

This paper presents a neural architecture MVDiffusion++ for 3D object reconstruction that
synthesizes dense and high-resolution views of an object given one or a few images without …

Generating high-quality 3D assets from a given image is highly desirable in various
applications such as AR/VR. Recent advances in single-image 3D generation explore feed …

This paper presents a novel method for building scalable 3D generative models utilizing pre-
trained video diffusion models. The primary obstacle in developing foundation 3D …

In recent years, 3D Gaussian splatting has emerged as a powerful technique for 3D
reconstruction and generation, known for its fast and high-quality rendering capabilities …

We present InstantMesh, a feed-forward framework for instant 3D mesh generation from a
single image, featuring state-of-the-art generation quality and significant training scalability …

We study the problem of single-image zero-shot 3D shape reconstruction. Recent works
learn zero-shot shape reconstruction through generative modeling of 3D assets but these …

This technical report introduces TripoSR, a 3D reconstruction model leveraging transformer
architecture for fast feed-forward 3D generation, producing 3D mesh from a single image in …

Most text-to-3D generators build upon off-the-shelf text-to-image models trained on billions
of images. They use variants of Score Distillation Sampling (SDS), which is slow, somewhat …