This paper introduces Motion Personalization, a new task that generates personalized motions aligned with text descriptions using several basic motions containing Persona. To support this novel task, we introduce a new large-scale motion dataset called PerMo (PersonaMotion), which captures the unique personas of multiple actors. We also propose a multi-modal finetuning method of a pretrained motion diffusion model called PersonaBooth. PersonaBooth addresses two main challenges: i) A significant distribution gap between the persona-focused PerMo dataset and the pretraining datasets, which lack persona-specific data, and ii) the difficulty of capturing a consistent persona from the motions vary in content (action type). To tackle the dataset distribution gap, we introduce a persona token to accept new persona features and perform multi-modal adaptation for both text and visuals during finetuning. To capture a consistent persona, we incorporate a contrastive learning technique to enhance intra-cohesion among samples with the same persona. Furthermore, we introduce a context-aware fusion mechanism to maximize the integration of persona cues from multiple input motions. PersonaBooth outperforms state-of-the-art motion style transfer methods, establishing a new benchmark for motion personalization.

This paper proposes a novel inpainting framework, omission-free inpainting, which ensures generating the desired object in the masked region. Despite recent advancements in text-driven and class-conditional inpainting models, they often fail to restore the missing object. To address this issue, the proposed framework includes a separate object generation stage, resulting in omission-free inpainting. The framework consists of three stages: background generation, object generation and refinement. The background generation stage restores a harmonious background with the surrounding pixels, while the object generation stage creates the desired object using a blending mask that allows the object to be influenced by the background’s color and brightness. Finally, the refinement stage blends the object and background to produce a visually realistic image. We compare the results qualitatively with the state-of-the-art methods, and our method outperforms the existing methods in CLIP score.