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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.10218 |
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| _version_ | 1866917294652784640 |
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| author | Rai, Gaurav Sharma, Ojaswa |
| author_facet | Rai, Gaurav Sharma, Ojaswa |
| contents | Sketch animation has emerged as a transformative technology, bridging art and science to create dynamic visual narratives across various fields such as entertainment, education, healthcare, and virtual reality. This survey explores recent trends and innovations in sketch animation, with a focus on methods that have advanced the state of the art. The paper categorizes and evaluates key methodologies, including keyframe interpolation, physics-based animation, data-driven, motion capture, and deep learning approaches. We examine the integration of artificial intelligence, real-time rendering, and cloud-based solutions, highlighting their impact on enhancing realism, scalability, and interactivity. Additionally, the survey delves into the challenges of computational complexity, scalability, and user-friendly interfaces, as well as emerging opportunities within metaverse applications and human-machine interaction. By synthesizing insights from a wide array of research, this survey aims to provide a comprehensive understanding of the current landscape and future directions of sketch animation, serving as a resource for both academics and industry professionals seeking to innovate in this dynamic field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10218 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sketch Animation: State-of-the-art Report Rai, Gaurav Sharma, Ojaswa Graphics Sketch animation has emerged as a transformative technology, bridging art and science to create dynamic visual narratives across various fields such as entertainment, education, healthcare, and virtual reality. This survey explores recent trends and innovations in sketch animation, with a focus on methods that have advanced the state of the art. The paper categorizes and evaluates key methodologies, including keyframe interpolation, physics-based animation, data-driven, motion capture, and deep learning approaches. We examine the integration of artificial intelligence, real-time rendering, and cloud-based solutions, highlighting their impact on enhancing realism, scalability, and interactivity. Additionally, the survey delves into the challenges of computational complexity, scalability, and user-friendly interfaces, as well as emerging opportunities within metaverse applications and human-machine interaction. By synthesizing insights from a wide array of research, this survey aims to provide a comprehensive understanding of the current landscape and future directions of sketch animation, serving as a resource for both academics and industry professionals seeking to innovate in this dynamic field. |
| title | Sketch Animation: State-of-the-art Report |
| topic | Graphics |
| url | https://arxiv.org/abs/2510.10218 |