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cs.CV2026
Mamoda2.5: Enhancing Unified Multimodal Model with DiT-MoE
Yangming Shi, Shixiang Zhu, Tao Shen +14
We present Mamoda2.5, a unified AR-Diffusion framework that seamlessly integrates multimodal understanding and generation within a single architecture. To efficiently enhance the m…
cs.CV2026
Many-for-Many: Unify the Training of Multiple Video and Image Generation and Manipulation Tasks
Ruibin Li, Tao Yang, Yangming Shi +4
Diffusion models have shown impressive performance in many visual generation and manipulation tasks. Many existing methods focus on training a model for a specific task, especially…
cs.CV2024
Factorized-Dreamer: Training A High-Quality Video Generator with Limited and Low-Quality Data
Tao Yang, Yangming Shi, Yunwen Huang +3
Text-to-video (T2V) generation has gained significant attention due to its wide applications to video generation, editing, enhancement and translation, \etc. However, high-quality…