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
Reconstructing 3D scenes from a single image is a fundamental challenge in computer vision, with broad applications in virtual reality, robotics, and content creation. Recent methods achieve outstanding performance by leveraging camera-controlled video diffusion models, but rely on iterative diffusion sampling, which greatly limits their practical deployment.
arXiv:2404.17569v4 Announce Type: replace
Abstract: This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to gen...
By Shangzhan Zhang, Sida Peng, Tao Xu, Yuanbo Yang, Tianrun Chen, Nan Xue, Yujun Shen, Hujun Bao, Ruizhen Hu, Xiaowei Zhou
arXiv:2602.19202v3 Announce Type: replace
Abstract: Event cameras excel at high-speed, low-power, and high-dynamic-range scene perception. However, as they fundamentally record only relative intensit...
By Gang Xu, Zhiyu Zhu, Junhui Hou
arXiv:2605.12957v2 Announce Type: replace
Abstract: Recent developments in generative models and large-scale datasets have substantially advanced 3D world generation, facilitating a broad range of do...
By Hanxin Zhu, Cong Wang, Peiyan Tu, Jiayi Luo, Tianyu He, Xin Jin, Zhibo Chen
arXiv:2608.20515v1 Announce Type: new
Abstract: Generative video compression can recover rich visual details at low bitrates, but simultaneously achieving high temporal consistency and low inference...
By Wenzhuo Ma, Zhenzhong Chen
arXiv:2609.08153v1 Announce Type: new
Abstract: Generative diffusion models have emerged as a class of powerful techniques for various imaging applications, including but not limited to synthesis, re...
By Nian Wu, Nivetha Jayakumar, Jiarui Xing, Miaomiao Zhang
We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios.
arXiv:2609.10363v1 Announce Type: new
Abstract: SceneHI is a framework that lifts high-resolution, illumination-aware priors from 2D diffusion models to perform 3D texture synthesis. It is the first...
By Athanasios Tragakis, Marco Aversa, Daniela Ivanova, Chaitanya Kaul, Roderick Murray-Smith, Daniele Faccio, Paul Henderson
arXiv:2609.39709v1 Announce Type: new
Abstract: Recent diffusion-based pipelines have achieved promising progress in image-to-3D synthesis. However, generating high-fidelity details remains challengi...
By Junyu Li, Qiuyu Chen, Pengcheng Wang, Shiqi Yang, Alexandra Gomez-Villa, Joost van de Weijer, Ruilin Li, Kai Wang
arXiv:2605.04412v3 Announce Type: replace
Abstract: 3D asset generation plays a pivotal role in fields such as gaming and virtual reality, enabling the rapid synthesis of high-fidelity 3D objects fro...
By Yiran Qiao, Yiren Lu, Yunlai Zhou, Disheng Liu, Linlin Hou, Rui Yang, Yu Yin, Jing Ma
While 3D Gaussian Splatting (3DGS) has revolutionized 3D reconstruction and novel-view synthesis, scenarios with limited input views often lead to poor reconstruction quality and artifacts in rendered...