1 citations · 2 across the 11 of their papers we have counts for
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WeVisOne-VL: Unified Visual Perception from Objects to Pixels
Tianming Liang, Junyi Pan, Yuxi Li +5
Existing vision-language models (VLMs) remain unreliable for fine-grained visual perception. In this report, we present WeVisOne-VL (WeChat Vision All-in-One), a powerful VLM that…
SyncReward: Human-Aligned Synchronization Reward for Audio-Visual Generation
Kaisi Guan, Ying Ba, Wenjing Wang +4
Recent years have witnessed rapid progress in joint audio--visual generation. However, audio--visual synchronization remains a challenge, as generated speech and sound often tempor…
WeVisDoc: From Coverage to Capability for Robust End-to-End Document Parsing
Hao Yu, Kang Liu, Linnan Zhao +4
Document parsing converts document images into structured content and requires reliable performance across diverse layouts and acquisition conditions. Yet training corpora are bias…
Tether the Subject, Release the Scene: Query-Aware Memory Routing for Long-Horizon Autoregressive Video Generation
Chen Li, Peng Zhang, Hanyu Zhou +5
Streaming autoregressive video models generate long videos chunk by chunk, using historical memory to maintain consistency. Existing methods typically expose subject and scene quer…
FilmWorld: Agentic Novel-to-Film Generation through Dynamic Cinematic World Modeling
Jialong Zuo, Haotong Zuo, Shiwei Zhang +5
Translating novels into films poses a grand challenge for generative artificial intelligence, requiring conversion of abstract literary prose into long-form, multi-scene visual nar…
Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing
Feng Wang, Canmiao Fu, Zhipeng Huang +3
Recent unified multimodal models show a single architecture can jointly perform vision/language understanding and image generation/editing. However, they repeatedly feed all histor…
WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens
Yiwei Guo, Shaobin Zhuang, Zhipeng Huang +4
Building a unified visual tokenizer is essential for bridging the gap between visual understanding and generation. Yet existing approaches struggle with the inherent conflict betwe…
Text-guided Visual Prompt DINO for Generic Segmentation
Yuchen Guan, Chong Sun, Canmiao Fu +3
Recent advancements in multimodal vision models have highlighted limitations in late-stage feature fusion and suboptimal query selection for hybrid prompts open-world segmentation,…
WeTok: Powerful Discrete Tokenization for High-Fidelity Visual Reconstruction
Shaobin Zhuang, Yiwei Guo, Canmiao Fu +7
Visual tokenizer is a critical component for vision generation. However, the existing tokenizers often face unsatisfactory trade-off between compression ratios and reconstruction f…
Video-GPT via Next Clip Diffusion
Shaobin Zhuang, Zhipeng Huang, Ying Zhang +6
GPT has shown its remarkable success in natural language processing. However, the language sequence is not sufficient to describe spatial-temporal details in the visual world. Alte…
TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision
Shaobin Zhuang, Yiwei Guo, Yanbo Ding +7
Diffusion models have driven the advancement of vision generation over the past years. However, it is often difficult to apply these large models in downstream tasks, due to massiv…
WeGen: A Unified Model for Interactive Multimodal Generation as We Chat
Zhipeng Huang, Shaobin Zhuang, Canmiao Fu +7
Existing multimodal generative models fall short as qualified design copilots, as they often struggle to generate imaginative outputs once instructions are less detailed or lack th…
Get In Video: Add Anything You Want to the Video
Shaobin Zhuang, Zhipeng Huang, Binxin Yang +7
Video editing increasingly demands the ability to incorporate specific real-world instances into existing footage, yet current approaches fundamentally fail to capture the unique v…