GaussianAnything: interactive point cloud latent diffusion for 3D generation

• Main Authors: Lan, Yushi, Zhou, Shangchen, Lyu, Zhaoyang, Hong, Fangzhou, Yang, Shuai, Dai, Bo, Pan, Xingang, Loy, Chen Change
• Other Authors: College of Computing and Data Science
• Format: Conference or Workshop Item
• Language: English
• Published: 2025
• Subjects: Computer and Information Science; 3D generation; Images
• Online Access: https://hdl.handle.net/10356/182593; http://arxiv.org/abs/2411.08033v1; https://iclr.cc/; https://iclr.cc/FAQ/Proceedings

While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper introduces a novel 3D generation framework that addresses these challenges, offering scalable, high-quality 3D generation...