arXiv:2603. 03143v2 Announce Type: replace-cross Abstract: Leveraging the priors of 2D diffusion models for 3D editing has emerged as a promising paradigm.
By Jiyuan Wang, Chunyu Lin, Lei Sun, Zhi Cao, Yuyang Yin, Lang Nie, Zhenlong Yuan, Xiangxiang Chu, Yunchao Wei, Kang Liao, Guosheng Lin
arXiv:2610.27049v1 Announce Type: new
Abstract: Recent advances in unified visual editing have begun to consolidate image and video editing within a single model, while 3D scene editing remains large...
By Jing Wang, Yanlin Li, Mong-Li Lee, Wynne Hsu, Shengqiong Wu, Hao Fei
The paper introduces SAGE, a framework that adapts 3D geometric foundation models using weak supervision from raw Internet videos. SAGE employs a hierarchical mining pipeline to generate informative training trajectories, sparse geometric anchoring via SfM point clouds, and dense differentiable consistency through 3D Gaussian rendering, while also replaying a small amount of supervised data to avoid catastrophic forgetting. Experiments on two different backbones show that SAGE reduces Chamfer Distance on unseen benchmarks by 20–42% and consistently improves performance, demonstrating that Internet video can serve as a scalable source for geometric learning.
By Zihui Gao, Ke Liu, Donny Y. Chen, Duochao Shi, Guosheng Lin, Jia-Wang Bian, Hao Chen, Chunhua Shen
arXiv:2610.35756v1 Announce Type: new
Abstract: Recent advancements in text-guided 3D editing, particularly with 3D Gaussian Splatting (3DGS), have shown promise but still lag behind the flexibility...
By Yanming Yang, Chenxi Song, Tong Zhao, Juntao Xu, Wenhao Rao, Yiwen Chen, Yiwei Wang, Chi Zhang
OREO is a framework that improves the visual fidelity of 3D generation models by using on-the-fly rendered and edited 2D views as pseudo-targets. It introduces a dynamic optimization loop where a 2D diffusion model refines rendered views, preserving geometry, viewpoint, and content while enhancing realism. These refined views serve as high‑quality supervision, enabling the 3D generator to learn from its own outputs and progressively improve its visual quality, outperforming pre‑trained baselines.
By Zhiyuan Ma, Wenbo Hu, Wang Zhao, Pengfei Wang, Ying Shan, Lei Zhang
arXiv:2608.23549v1 Announce Type: new
Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
By Khiem Vuong, Deva Ramanan, Srinivasa Narasimhan