AnimeShooter: A Multi-Shot Animation Dataset for Reference-Guided Video
  Generation
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.
683fb3719f37285365b080c9
Lu Qiu
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2025-06-04T08:56:04.988Z
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/avatars/a48464044e9eb11a2bc062be05d9aa9a.svg
qiulu
user
qiulu66
683fb3719f37285365b080ca
Yizhuo Li
claimed_verified
2025-06-04T08:56:02.456Z
630b094f8b327c7b8b94d24c
https://cdn-avatars.huggingface.co/v1/production/uploads/630b094f8b327c7b8b94d24c/Fd0ugLOHJZIi8oUZScOmX.jpeg
Yizhuo Li
user
liyz
683fb3719f37285365b080cb
Yuying Ge
683fb3719f37285365b080cc
Yixiao Ge
683fb3719f37285365b080cd
Ying Shan
683fb3719f37285365b080ce
Xihui Liu
AnimeShooter: A Multi-Shot Animation Dataset for Reference-Guided Video
  Generation
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.
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.
AnimeShooter reference-guided multimodal large language models (MLLMs) video diffusion models hierarchical annotations visual consistency cross-shot visual consistency reference visual guidance