The paper highlights a significant quality gap between high-quality image datasets and the lower-quality publicly available video datasets, impacting animation pipeline performance.
The authors propose a Domain Adapter to alleviate negative effects stemming from the lower quality of video training datasets, aiming to bridge the quality domain gap.
Motion priors learned from video datasets can be improved through structured methodologies including the introduction of MotionLora, which targets new motion patterns effectively.
The paper emphasizes that directly training on raw video data can lead to quality issues, hence the need for strategies to improve animation generation quality.
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