Zolly: Zoom focal length correctly for perspective-distorted human mesh reconstruction
As it is hard to calibrate single-view RGB images in the wild, existing 3D human mesh
reconstruction (3DHMR) methods either use a constant large focal length or estimate one …
reconstruction (3DHMR) methods either use a constant large focal length or estimate one …
LEMON: Learning 3D Human-Object Interaction Relation from 2D Images
Learning 3D human-object interaction relation is pivotal to embodied AI and interaction
modeling. Most existing methods approach the goal by learning to predict isolated …
modeling. Most existing methods approach the goal by learning to predict isolated …
NeuralGF: unsupervised point normal estimation by learning neural gradient function
Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The
state-of-the-art methods rely on priors of fitting local surfaces learned from normal …
state-of-the-art methods rely on priors of fitting local surfaces learned from normal …
Robust zero level-set extraction from unsigned distance fields based on double covering
In this paper, we propose a new method, called DoubleCoverUDF, for extracting the zero
level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and …
level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and …
Deep3DSketch-im: rapid high-fidelity AI 3D model generation by single freehand sketches
The rise of artificial intelligence generated content (AIGC) has been remarkable in the
language and image fields, but artificial intelligence (AI) generated three-dimensional (3D) …
language and image fields, but artificial intelligence (AI) generated three-dimensional (3D) …
Deep3dsketch+: Rapid 3d modeling from single free-hand sketches
The rapid development of AR/VR brings tremendous demands for 3D content. While the
widely-used Computer-Aided Design (CAD) method requires a time-consuming and labor …
widely-used Computer-Aided Design (CAD) method requires a time-consuming and labor …
Neural gradient learning and optimization for oriented point normal estimation
We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient
vectors with consistent orientation from 3D point clouds for normal estimation. It has …
vectors with consistent orientation from 3D point clouds for normal estimation. It has …
PointVST: Self-supervised pre-training for 3d point clouds via view-specific point-to-image translation
The past few years have witnessed the great success and prevalence of self-supervised
representation learning within the language and 2D vision communities. However, such …
representation learning within the language and 2D vision communities. However, such …
CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets
In the realm of digital creativity, our potential to craft intricate 3D worlds from imagination is
often hampered by the limitations of existing digital tools, which demand extensive expertise …
often hampered by the limitations of existing digital tools, which demand extensive expertise …
Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation
We propose a novel method called SHS-Net for point cloud normal estimation by learning
signed hyper surfaces, which can accurately predict normals with global consistent …
signed hyper surfaces, which can accurately predict normals with global consistent …