9 papers
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…
Stream-T1: Test-Time Scaling for Streaming Video Generation
Yijing Tu, Shaojin Wu, Mengqi Huang +4
While Test-Time Scaling (TTS) offers a promising direction to enhance video generation without the surging costs of training, current test-time video generation methods based on di…
Stream-R1: Reliability-Perplexity Aware Reward Distillation for Streaming Video Generation
Bin Wu, Mengqi Huang, Shaojin Wu +4
Distillation-based acceleration has become foundational for making autoregressive streaming video diffusion models practical, with distribution matching distillation (DMD) as the d…
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…
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…