arXiv:2607.13808v2 Announce Type: replace
Abstract: Neural radiance representations in Gaussian Splatting (GS) deliver high-fidelity color detail but impose substantial rendering overhead from networ...
By Neel Kelkar, Simon Niedermayr, Kaloian Petkov, Klaus Engel, R\"udiger Westermann
arXiv:2609.23169v1 Announce Type: new
Abstract: High-quality texture generation is essential for creating realistic and production-ready 3D assets. Recent multi-view diffusion methods have shown prom...
By Yibo Zhang, Ze Yuan, Nan Cao, Li Zhang, Yan-Pei Cao, Yuan-Chen Guo, Rui Ma
arXiv:2609.23606v1 Announce Type: new
Abstract: Mesh texture compression typically relies on 2D UV atlases, whose chart discontinuities and mapping overhead can limit coding efficiency. To tackle thi...
By Jianqiang Wang, Junhui Hou, Siyu Ren, Weiyao Lin, Wenping Wang
arXiv:2607. 17842v1 Announce Type: cross Abstract: Recent breakthroughs in 3D Gaussian Splatting (3DGS) have advanced neural rendering with high fidelity and speed.
By Tingjia Zhang, Bo Chen, Shengzhong Liu, Fan Wu, Guihai Chen
arXiv:2610.29691v1 Announce Type: new
Abstract: Generative image compression (GIC) delivers strong perceptual quality at low bitrates. However, GIC methods with strong perceptual quality often rely o...
By Hao Cao, Yinhuan Huang, Wenqi Guo, Zhijin Qin, Jungong Han
The paper introduces a new learning paradigm for visual generation that separates knowledge acquisition and high-fidelity rendering. Autoregressive pretraining is used to acquire semantic knowledge, while diffusion models are employed afterward for detailed image synthesis. The authors demonstrate that this approach improves learning efficiency and final generation quality, and they provide compute‑optimal scaling laws that enable training a 3B‑parameter text‑to‑image model with only 6,600 H100 GPU‑hours, achieving state‑of‑the‑art scores on GenEval and DPG‑Bench.
By Yucheng Mao, Chongjian Ge, Zhuowen Tu, Hao Tan, Hanwen Jiang