9 citations · 12 across the 11 of their papers we have counts for
11 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-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…
Step-Video-TI2V Technical Report: A State-of-the-Art Text-Driven Image-to-Video Generation Model
Haoyang Huang, Guoqing Ma, Nan Duan +51
We present Step-Video-TI2V, a state-of-the-art text-driven image-to-video generation model with 30B parameters, capable of generating videos up to 102 frames based on both text and…
Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model
Guoqing Ma, Haoyang Huang, Kun Yan +112
We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model with 30B parameters and the ability to generate videos up to 204 frames in length. A deep compression…
Using Left and Right Brains Together: Towards Vision and Language Planning
Jun Cen, Chenfei Wu, Xiao Liu +6
Large Language Models (LLMs) and Large Multi-modality Models (LMMs) have demonstrated remarkable decision masking capabilities on a variety of tasks. However, they inherently opera…
StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
Zecheng Tang, Chenfei Wu, Zekai Zhang +8
To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model…
ORES: Open-vocabulary Responsible Visual Synthesis
Minheng Ni, Chenfei Wu, Xiaodong Wang +4
Avoiding synthesizing specific visual concepts is an essential challenge in responsible visual synthesis. However, the visual concept that needs to be avoided for responsible visua…
DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory
Shengming Yin, Chenfei Wu, Jian Liang +4
Controllable video generation has gained significant attention in recent years. However, two main limitations persist: Firstly, most existing works focus on either text, image, or…
NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation
Shengming Yin, Chenfei Wu, Huan Yang +13
In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation. Most current work generates long videos segment by segment seq…