# PersonaAnimator: Personalized Motion Transfer from Unconstrained Videos

## TL;DR

PersonaAnimator enables personalized, physically plausible motion transfer directly from unconstrained videos, overcoming limitations of style learning and reliance on motion capture data, thus advancing video-based character animation.

## Contribution

The paper introduces PersonaAnimator, a novel framework for personalized motion transfer from unconstrained videos, along with the first dataset PersonaVid for this task.

## Key findings

- Outperforms state-of-the-art motion transfer methods
- Sets new benchmark for Video-to-Video Motion Personalization
- Enforces physical plausibility in generated motions

## Abstract

Recent advances in motion generation show remarkable progress. However, several limitations remain: (1) Existing pose-guided character motion transfer methods merely replicate motion without learning its style characteristics, resulting in inexpressive characters. (2) Motion style transfer methods rely heavily on motion capture data, which is difficult to obtain. (3) Generated motions sometimes violate physical laws. To address these challenges, this paper pioneers a new task: Video-to-Video Motion Personalization. We propose a novel framework, PersonaAnimator, which learns personalized motion patterns directly from unconstrained videos. This enables personalized motion transfer. To support this task, we introduce PersonaVid, the first video-based personalized motion dataset. It contains 20 motion content categories and 120 motion style categories. We further propose a Physics-aware Motion Style Regularization mechanism to enforce physical plausibility in the generated motions. Extensive experiments show that PersonaAnimator outperforms state-of-the-art motion transfer methods and sets a new benchmark for the Video-to-Video Motion Personalization task.