title: AnimeShooter: A Multi-Shot Animation Dataset for Reference-Guided Video
    Generation
summary: Recent advances in AI-generated content (AIGC) have significantly accelerated
  animation production. To produce engaging animations, it is essential to
  generate coherent multi-shot video clips with narrative scripts and character
  references. However, existing public datasets primarily focus on real-world
  scenarios with global descriptions, and lack reference images for consistent
  character guidance. To bridge this gap, we present AnimeShooter, a
  reference-guided multi-shot animation dataset. AnimeShooter features
  comprehensive hierarchical annotations and strong visual consistency across
  shots through an automated pipeline. Story-level annotations provide an
  overview of the narrative, including the storyline, key scenes, and main
  character profiles with reference images, while shot-level annotations
  decompose the story into consecutive shots, each annotated with scene,
  characters, and both narrative and descriptive visual captions. Additionally, a
  dedicated subset, AnimeShooter-audio, offers synchronized audio tracks for each
  shot, along with audio descriptions and sound sources. To demonstrate the
  effectiveness of AnimeShooter and establish a baseline for the reference-guided
  multi-shot video generation task, we introduce AnimeShooterGen, which leverages
  Multimodal Large Language Models (MLLMs) and video diffusion models. The
  reference image and previously generated shots are first processed by MLLM to
  produce representations aware of both reference and context, which are then
  used as the condition for the diffusion model to decode the subsequent shot.
  Experimental results show that the model trained on AnimeShooter achieves
  superior cross-shot visual consistency and adherence to reference visual
  guidance, which highlight the value of our dataset for coherent animated video
  generation.
paper_authors._id: 683fb3719f37285365b080c9
paper_authors.name: Lu Qiu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:56:04.988Z
paper_authors._id: 672a037c19f1f942483f680c
paper_authors.avatarUrl: /avatars/a48464044e9eb11a2bc062be05d9aa9a.svg
paper_authors.fullname: qiulu
paper_authors.type: user
paper_authors.user: qiulu66
paper_authors._id: 683fb3719f37285365b080ca
paper_authors.name: Yizhuo Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:56:02.456Z
paper_authors._id: 630b094f8b327c7b8b94d24c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/630b094f8b327c7b8b94d24c/Fd0ugLOHJZIi8oUZScOmX.jpeg
paper_authors.fullname: Yizhuo Li
paper_authors.type: user
paper_authors.user: liyz
paper_authors._id: 683fb3719f37285365b080cb
paper_authors.name: Yuying Ge
paper_authors._id: 683fb3719f37285365b080cc
paper_authors.name: Yixiao Ge
paper_authors._id: 683fb3719f37285365b080cd
paper_authors.name: Ying Shan
paper_authors._id: 683fb3719f37285365b080ce
paper_authors.name: Xihui Liu
paper_title: AnimeShooter: A Multi-Shot Animation Dataset for Reference-Guided Video
    Generation
paper_summary: Recent advances in AI-generated content (AIGC) have significantly accelerated
  animation production. To produce engaging animations, it is essential to
  generate coherent multi-shot video clips with narrative scripts and character
  references. However, existing public datasets primarily focus on real-world
  scenarios with global descriptions, and lack reference images for consistent
  character guidance. To bridge this gap, we present AnimeShooter, a
  reference-guided multi-shot animation dataset. AnimeShooter features
  comprehensive hierarchical annotations and strong visual consistency across
  shots through an automated pipeline. Story-level annotations provide an
  overview of the narrative, including the storyline, key scenes, and main
  character profiles with reference images, while shot-level annotations
  decompose the story into consecutive shots, each annotated with scene,
  characters, and both narrative and descriptive visual captions. Additionally, a
  dedicated subset, AnimeShooter-audio, offers synchronized audio tracks for each
  shot, along with audio descriptions and sound sources. To demonstrate the
  effectiveness of AnimeShooter and establish a baseline for the reference-guided
  multi-shot video generation task, we introduce AnimeShooterGen, which leverages
  Multimodal Large Language Models (MLLMs) and video diffusion models. The
  reference image and previously generated shots are first processed by MLLM to
  produce representations aware of both reference and context, which are then
  used as the condition for the diffusion model to decode the subsequent shot.
  Experimental results show that the model trained on AnimeShooter achieves
  superior cross-shot visual consistency and adherence to reference visual
  guidance, which highlight the value of our dataset for coherent animated video
  generation.
paper_ai_summary: AnimeShooter, a reference-guided multi-shot animation dataset, enhances coherent animated video generation by incorporating comprehensive hierarchical annotations and visual consistency, and AnimeShooterGen leverages MLLMs and video diffusion models to achieve superior results.
paper_ai_keywords: AnimeShooter reference-guided multimodal large language models (MLLMs) video diffusion models hierarchical annotations visual consistency cross-shot visual consistency reference visual guidance