Referenced Papers (7)
Meshtron: High-fidelity, artist-like 3D mesh generation at scale
Zekun Hao, David W Romero, Tsung-Yi Lin, Ming-Yu Liu
ArXiv, 2024
"This paper is the main subject of the academic talk, presenting a novel autoregressive mesh generation model capable of creating high-quality, editable 3D meshes."
Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models
Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, Daniel Cohen-Or
ACM Trans. Graph., 2023
"This paper is cited as the source for a video clip demonstrating text-to-video generation using AI."
Exploring patient multimorbidity and complexity using health insurance claims data: A cluster analysis approach (preprint)
Anna Nicolet, Dan Assouline, Marie-Annick Le Pogam, Clémence Perraudin, Christophe Bagnoud, Joël Wagner
JMIR Preprints, 2021
"This paper demonstrates a previous technique for generating 3D geometry, highlighting its limitations in producing artist-like meshes."
Meshtron: High-fidelity, artist-like 3D mesh generation at scale
Zekun Hao, David W Romero, Tsung-Yi Lin, Ming-Yu Liu
arXiv [cs.GR], 2024
"This paper presents the 'MeshAnything v2' technique, serving as a 'previous best' comparison to the new Meshtron method, showcasing its issues with geometric quality and editability."
Point-E: A system for generating 3D point clouds from complex prompts
Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, Mark Chen
ArXiv, 2022
"This paper is cited as an example of an AI technique that can easily generate point clouds from text prompts, which can then be converted into meshes by Meshtron."
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control
Guy Tevet, Sigal Raab, Setareh Cohan, Daniele Reda, Zhengyi Luo, Xue Bin Peng
arXiv [cs.CV], 2024
"This paper is cited as an example of current advancements in generating animated models, where mesh detail can be adjusted for real-time applications or high-quality rendering."
Training-free point cloud recognition based on geometric and semantic information fusion
Yan Chen, Di Huang, Zhichao Liao, Xi Cheng, Xinghui Li, Long Zeng
arXiv [cs.CV], 2024
"This paper is referenced for its work on noise in 3D data, particularly in animation, showcasing methods to remove noise from generated models."