DualReal: Adaptive Joint Training for Lossless Identity-Motion Fusion in Video Customization

Customized text-to-video generation with pre-trained large-scale models has recently garnered significant attention through focusing on identity and motion consistency. Existing works typically follow the isolated customized paradigm, where the subject identity or motion dynamics are customized exclusively. However, this paradigm completely ignores the intrinsic $\textbf{mutual constraints and synergistic interdependencies}$ between identity and motion, resulting in identity-motion conflicts throughout the generation process that systematically degrades. To address this, we introduce $\textbf{DualReal}$, a novel framework that, employs adaptive joint training to collaboratively construct interdependencies between dimensions. Specifically, DualReal is composed of two units: (1) $\textbf{Dual-aware Adaptation}$ dynamically selects a training phase ($\textit{i.e.}$, identity or motion), learns the current information guided by the frozen dimension prior, and employs a regularization strategy to avoid knowledge leakage; (2) $\textbf{StageBlender Controller}$ leverages the denoising stages and Diffusion Transformer depths to guide different dimensions with adaptive granularity, avoiding conflicts at various stages and ultimately achieving lossless fusion of identity and motion patterns. We constructed a more comprehensive evaluation benchmark than existing methods. The experimental results show that DualReal improves CLIP-I and DINO-I metrics by $\textbf{21.7}$% and $\textbf{31.8}$% on average, and achieves top performance on nearly all motion quality metrics.