Search Results for author:
Found 7 papers, 2 papers with code
InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models
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.
Advances in 3D Generation: A Survey
In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications.
Sparse3D: Distilling Multiview-Consistent Diffusion for Object Reconstruction from Sparse Views
While recent techniques employ image diffusion models for generating plausible images at novel viewpoints or for distilling pre-trained diffusion priors into 3D representations using score distillation sampling (SDS), these methods often struggle to simultaneously achieve high-quality, consistent, and detailed results for both novel-view synthesis (NVS) and geometry.
ID-Pose: Sparse-view Camera Pose Estimation by Inverting Diffusion Models
ID-Pose adds a noise to one image, and predicts the noise conditioned on the other image and a hypothesis of the relative pose.
InstructP2P: Learning to Edit 3D Point Clouds with Text Instructions
Enhancing AI systems to perform tasks following human instructions can significantly boost productivity.
SparseGNV: Generating Novel Views of Indoor Scenes with Sparse Input Views
We study to generate novel views of indoor scenes given sparse input views.
Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models
Specifically, we first generate a high-quality 3D shape from the input text in the text-to-shape stage as a 3D shape prior.
