4 citations · 4 across the 10 of their papers we have counts for
11 papers
FilmBench: A Film-Grade Benchmark for Cinematic Video Generation
Shengyi Wang, Niantong Li, Guangzheng Hu +27
Progress in video generation keeps narrowing the visual gap between AI-generated and professionally produced footage, yet most benchmarks still draw prompts from web sources or LLM…
Lance: Unified Multimodal Modeling by Multi-Task Synergy
Fengyi Fu, Mengqi Huang, Shaojin Wu +10
We present Lance, a lightweight native unified model supporting multimodal understanding, generation, and editing for both images and videos. Rather than relying on model capacity…
Seedance 2.0: Advancing Video Generation for World Complexity
Team Seedance, De Chen, Liyang Chen +168
Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro…
Order Is Not Layout: Order-to-Space Bias in Image Generation
Yongkang Zhang, Zonglin Zhao, Yuechen Zhang +3
We study a systematic bias in modern image generation models: the mention order of entities in text spuriously determines spatial layout and entity--role binding. We term this phen…
UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward
Yufeng Cheng, Wenxu Wu, Shaojin Wu +3
Recent advancements in image customization exhibit a wide range of application prospects due to stronger customization capabilities. However, since we humans are more sensitive to…
USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning
Shaojin Wu, Mengqi Huang, Yufeng Cheng +5
Existing literature typically treats style-driven and subject-driven generation as two disjoint tasks: the former prioritizes stylistic similarity, whereas the latter insists on su…
Less-to-More Generalization: Unlocking More Controllability by In-Context Generation
Shaojin Wu, Mengqi Huang, Wenxu Wu +3
Although subject-driven generation has been extensively explored in image generation due to its wide applications, it still has challenges in data scalability and subject expansibi…
DreamO: A Unified Framework for Image Customization
Chong Mou, Yanze Wu, Wenxu Wu +15
Recently, extensive research on image customization (e.g., identity, subject, style, background, etc.) demonstrates strong customization capabilities in large-scale generative mode…
VMix: Improving Text-to-Image Diffusion Model with Cross-Attention Mixing Control
Shaojin Wu, Fei Ding, Mengqi Huang +2
While diffusion models show extraordinary talents in text-to-image generation, they may still fail to generate highly aesthetic images. More specifically, there is still a gap betw…
RealCustom++: Representing Images as Real Textual Word for Real-Time Customization
Zhendong Mao, Mengqi Huang, Fei Ding +3
Given a text and an image of a specific subject, text-to-image customization aims to generate new images that align with both the text and the subject's appearance. Existing works…
DreamTuner: Single Image is Enough for Subject-Driven Generation
Miao Hua, Jiawei Liu, Fei Ding +3
Diffusion-based models have demonstrated impressive capabilities for text-to-image generation and are expected for personalized applications of subject-driven generation, which req…