6 citations · 14 across the 15 of their papers we have counts for
17 papers
Qwen-Image-Agent: Bridging the Context Gap in Real-World Image Generation
Zekai Zhang, Jiahao Li, Jie Zhang +18
While text-to-image (T2I) models have achieved remarkable progress, they struggle with real-world requests that are often underspecified, implicit, or dependent on up-to-date knowl…
Qwen-Image-2.0-RL Technical Report
Yixian Xu, Kaiyuan Gao, Yuxiang Chen +25
We present Qwen-Image-2.0-RL, a post-training pipeline that applies reinforcement learning from human feedback (RLHF) and on-policy distillation (OPD) to improve both the visual qu…
Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation
Niantong Li, Guangzheng Hu, Weixu Qiao +35
Text-to-Image generation has evolved from basic image synthesis into a frequently used core capability in professional creative workflows, where simple text-image alignment can no…
Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation
Jie Zhang, Xiaoyue Chen, Anzhe Chen +36
We introduce Qwen-RobotWorld, a language-conditioned video world model for embodied intelligence. With natural language as a unified action interface, it predicts physically ground…
Qwen-Image-Flash: Rethinking the Training Recipe for Few-Step Distillation
Tianhe Wu, Kun Yan, Zikai Zhou +23
Few-step distillation has emerged as a critical component in the development of advanced visual generative foundation models, substantially reducing inference overhead while enabli…
Qwen-Image-VAE-2.0 Technical Report
Zekai Zhang, Deqing Li, Kuan Cao +27
We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs) that achieve significant advances in both reconstruction fidelity and diffusability. To a…
Qwen-Image-2.0 Technical Report
Bing Zhao, Chenfei Wu, Deqing Li +72
We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite rece…
GVLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning
Wenbo Hu, Jingli Lin, Yilin Long +7
Vision-Language Models (VLMs) still lack robustness in spatial intelligence, demonstrating poor performance on spatial understanding and reasoning tasks. We attribute this gap to t…
ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting
Ruijie Zhu, Mulin Yu, Linning Xu +5
3D Gaussian Splatting is renowned for its high-fidelity reconstructions and real-time novel view synthesis, yet its lack of semantic understanding limits object-level perception. I…
MV-CoLight: Efficient Object Compositing with Consistent Lighting and Shadow Generation
Kerui Ren, Jiayang Bai, Linning Xu +4
Object compositing offers significant promise for augmented reality (AR) and embodied intelligence applications. Existing approaches predominantly focus on single-image scenarios o…
Virtualized 3D Gaussians: Flexible Cluster-based Level-of-Detail System for Real-Time Rendering of Composed Scenes
Xijie Yang, Linning Xu, Lihan Jiang +2
3D Gaussian Splatting (3DGS) enables the reconstruction of intricate digital 3D assets from multi-view images by leveraging a set of 3D Gaussian primitives for rendering. Its expli…
AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
Lihan Jiang, Yucheng Mao, Linning Xu +9
We introduce AnySplat, a feed forward network for novel view synthesis from uncalibrated image collections. In contrast to traditional neural rendering pipelines that demand known…
Scene4U: Hierarchical Layered 3D Scene Reconstruction from Single Panoramic Image for Your Immerse Exploration
Zilong Huang, Jun He, Junyan Ye +4
The reconstruction of immersive and realistic 3D scenes holds significant practical importance in various fields of computer vision and computer graphics. Typically, immersive and…
Horizon-GS: Unified 3D Gaussian Splatting for Large-Scale Aerial-to-Ground Scenes
Lihan Jiang, Kerui Ren, Mulin Yu +6
Seamless integration of both aerial and street view images remains a significant challenge in neural scene reconstruction and rendering. Existing methods predominantly focus on sin…
Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians
Kerui Ren, Lihan Jiang, Tao Lu +4
The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations. While demonstrating the potenti…
GSDF: 3DGS Meets SDF for Improved Rendering and Reconstruction
Mulin Yu, Tao Lu, Linning Xu +3
Presenting a 3D scene from multiview images remains a core and long-standing challenge in computer vision and computer graphics. Two main requirements lie in rendering and reconstr…
MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond
Yixuan Li, Lihan Jiang, Linning Xu +4
Neural radiance fields (NeRF) and its subsequent variants have led to remarkable progress in neural rendering. While most of recent neural rendering works focus on objects and smal…