Motif-Video 2B: Technical Report
Authors: name: Junghwan Lim, name: Wai Ting Cheung, name: Minsu Ha, name: Beomgyu Kim, name: Taewhan Kim, name: Haesol Lee, name: Dongpin Oh, name: Jeesoo Lee, name: Taehyun Kim, name: Minjae Kim, name: Sungmin Lee, name: Hyeyeon Cho, name: Dahye Choi, name: Jaeheui Her, name: Jaeyeon Huh, name: Hanbin Jung, name: Changjin Kang, name: Dongseok Kim, name: Jangwoong Kim, name: Youngrok Kim, name: Hyukjin Kweon, name: Hongjoo Lee, name: Jeongdoo Lee, name: Junhyeok Lee, name: Eunhwan Park, name: Yeongjae Park, name: Bokki Ryu, name: Dongjoo Weon
Organization: Motif Technologies

Training strong video generation models usually requires massive datasets, large parameter counts, and substantial compute. In this work, we ask whether strong text-to-video quality is possible at a much smaller budget: fewer than 10M clips and less than 100,000 H200 GPU hours. Our core claim is that part of the answer lies in how model capacity is organized, not only in how much of it is used. In video generation, prompt alignment, temporal consistency, and fine-detail recovery can interfere with one another when they are handled through the same pathway. Motif-Video 2B addresses this by separating these roles architecturally, rather than relying on scale alone. The model combines two key ideas. First, Shared Cross-Attention strengthens text control when video token sequences become long. Second, a three-part backbone separates early fusion, joint representation learning, and detail refinement. To make this design effective under a limited compute budget, we pair it with an efficient training recipe based on dynamic token routing and early-phase feature alignment to a frozen pretrained video encoder. Our analysis shows that later blocks develop clearer cross-frame attention structure than standard single-stream baselines. On VBench, Motif-Video~2B reaches 83.76\%, surpassing Wan2.1 14B while using 7times fewer parameters and substantially less training data. These results suggest that careful architectural specialization, combined with an efficiency-oriented training recipe, can narrow or exceed the quality gap typically associated with much larger video models.