Motif-Video 2B Technical Report Proposes Efficient Architecture for Text-to-Video Generation

A technical report introduces Motif-Video 2B, a model designed to achieve high-quality text-to-video generation with reduced computational demands. The research challenges the assumption that strong video generation necessitates massive datasets and extensive compute, proposing instead that architectural organization of model capacity is crucial. The model's core innovation lies in separating key functions—prompt alignment, temporal consistency, and fine-detail recovery—into distinct pathways to prevent interference. Two primary architectural ideas are implemented: Shared Cross-Attention enhances text control over long video token sequences, and a three-part backbone divides early fusion, joint representation learning, and detail refinement. This design aims to be effective under constrained budgets, specifically targeting training with fewer than 10 million video clips and under 100,000 H200 GPU hours. The work is presented in a cross-announcement on arXiv with the identifier 2604.16503v1, focusing on technical advancements rather than specific artistic applications. The report does not mention particular artists, institutions, or locations, concentrating solely on the model's technical framework and efficiency claims.