mlsanityData to 7 Oct 2026

MovieCharacter: A Tuning-Free Framework for Controllable Character Video Synthesis

arXiv 2410.20974 ↗First posted 28 Oct 2024cs.CVcited by 12

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Abstract (arXiv)

Recent advancements in character video synthesis still depend on extensive fine-tuning or complex 3D modeling processes, which can restrict accessibility and hinder real-time applicability. To address these challenges, we propose a simple yet effective tuning-free framework for character video synthesis, named MovieCharacter, designed to streamline the synthesis process while ensuring high-quality outcomes. Our framework decomposes the synthesis task into distinct, manageable modules: character segmentation and tracking, video object removal, character motion imitation, and video composition. This modular design not only facilitates flexible customization but also ensures that each component operates collaboratively to effectively meet user needs. By leveraging existing open-source models and integrating well-established techniques, MovieCharacter achieves impressive synthesis results without necessitating substantial resources or proprietary datasets. Experimental results demonstrate that our framework enhances the efficiency, accessibility, and adaptability of character video synthesis, paving the way for broader creative and interactive applications.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 34 of its 37 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

34 of 37 references.

References · 34

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22 more
  1. 2022Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generationcited by 958
  2. 2023Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animationcited by 683
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  15. 2024UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animationcited by 101
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 10; 2026: 2.

12 papers.