Index-AniSora: Bilibili’s Revolutionary Open-Source Anime Video Generation Model

The Dawn of a New Era in Animation Production

In today’s rapidly evolving landscape of AI-driven content creation, video generation technology has made quantum leaps. Yet a significant gap remained: specialized tools for anime and animation production. Recognizing this unmet need, Bilibili’s research team has unveiled Index-AniSora – a groundbreaking open-source model designed specifically for high-quality anime video generation.

This technological breakthrough represents a paradigm shift for animators, content creators, and anime enthusiasts worldwide. Unlike general video generation models, AniSora specializes in producing authentic Japanese anime styles, Chinese original animations, and diverse cartoon-inspired content with unprecedented fidelity.

Core Technical Architecture

Unified Spatiotemporal Mask Framework

The innovation powering AniSora is its spatiotemporal mask framework, which enables precise control over both temporal and spatial dimensions:

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This architecture supports three critical functions:

1. Image-to-video conversion: Transform static artwork into dynamic scenes
2. Precise timing control: First/last frame guidance, multi-frame interpolation
3. Localized motion: Region-specific animation through motion masks

Model Evolution Roadmap

Version	Foundation Model	Key Innovations	Deployment	Target Use Cases
V1.0	CogVideoX-5B	Initial spatiotemporal control	RTX 4090	Series clips, manga adaptations
V2.0	Wan2.1-14B	NPU support, distillation acceleration	Ascend 910B	VTuber content, animation PVs
V1.0_RL	RL-optimized	Human preference alignment	GPU clusters	Creative mad-style parodies

The V1.0 implementation builds on the CogVideoX-5B architecture, optimized for accessibility on consumer-grade hardware. V2.0 leverages Wan2.1-14B’s enhanced stability and introduces knowledge distillation techniques for accelerated processing while maintaining quality.

Performance Benchmarks

Comparative Analysis with Industry Models

VBench Evaluation Results:

Model	Motion Smoothness	Character Consistency	Visual Quality	Text-Video Alignment
Vidu	97.71	88.27	53.68	92.25
CogVideo	97.67	90.29	54.87	90.68
MiniMax	99.20	93.62	54.56	95.95
AniSora V1	99.34	96.99	54.31	97.52
AniSora V2	–	92.75	85.91	91.96

Specialized Anime Benchmark Results:

Model	Character Consistency	Visual Appeal	Motion Quality
Vidu-1.5	82.57	50.68	78.95
CogVideoX	83.07	39.59	73.07
AniSora V1	94.88	65.38	48.45
AniSora V2	92.75	85.91	50.34
Ground Truth	95.08	89.72	58.27

The data demonstrates AniSora’s exceptional character consistency – achieving 94.88/100 compared to ground truth’s 95.08. This capability addresses the persistent challenge of maintaining stable character features across frames in generated animation.

Practical Implementation Guide

System Requirements

Implementation	Minimum GPU	Recommended Setup
V1.0	RTX 3090 (24GB VRAM)	RTX 4090 (24GB VRAM)
V2.0	Ascend 910B	Ascend 910B cluster
RL Version	4x A100 (80GB)	8x A100 (80GB) cluster

Installation Process

1. Repository Setup:

git clone https://github.com/bilibili/Index-anisora.git
cd Index-anisora

2. Environment Configuration:

# For V1.0 implementation
cd anisoraV1_infer
pip install -r requirements.txt

# For V2.0 GPU implementation
cd anisoraV2_gpu
pip install -r requirements.txt

3. Model Acquisition:

# Hugging Face integration
from huggingface_hub import snapshot_download
snapshot_download(repo_id="IndexTeam/Index-anisora")

# ModelScope alternative
from modelscope import snapshot_download
snapshot_download('bilibili-index/Index-anisora')

Practical Implementation Examples

Basic Image-to-Video Conversion:

from anisora_pipeline import AniSoraGenerator

generator = AniSoraGenerator(version="v1.0")
result = generator.image_to_video(
    input_image="character_design.png",
    prompt="A samurai draws his katana slowly, cherry blossoms falling in background"
)
result.save("samurai_scene.mp4")

Advanced Temporal Control:

# Keyframe-guided interpolation
result = generator.temporal_control(
    first_frame="scene_start.jpg",
    last_frame="scene_end.jpg",
    keyframes=[(0.3, "mid_action.png")],
    prompt="Magical girl transformation sequence with sparkling effects"
)

Precision Motion Control:

# Region-specific animation
result = generator.spatial_control(
    input_image="fantasy_landscape.png",
    motion_mask="dragon_mask.png",
    prompt="Dragon flying over mountains with wing flapping motion"
)

Real-World Application Showcases

Image-to-Video Generation Examples

1. Dynamic Vehicle Scene
   Input: Character seated in moving car
   Prompt: “Figure waves backward, hair flowing in wind”
   Result: Seamless motion with natural hair physics

2. Cultural Ceremony
   Input: Two characters in traditional wedding attire
   Prompt: “Couple walking away holding red matrimonial ribbon”
   Result: Fluid movement with fabric dynamics

3. Action Sequence
   Input: Character mid-combat stance
   Prompt: “Warrior executes spinning kick with motion blur effect”
   Result: High-energy action with appropriate motion artifacts

Temporal Control Demonstrations

Control Type	Input Frames	Generated Output
Three-Point Guidance	First + Middle + Last	Smooth transition through complete action arc
Start/End Guidance	First + Last	Natural interpolation between key poses
Single Frame	Final Frame	Context-aware backward extrapolation

Spatial Control Implementations

Application	Motion Mask	Result
Selective Animation	Dragon wings only	Isolated wing movement with static background
Multi-Character Scenes	Individual character masks	Independent character motions in shared scene
Environmental Effects	Water surface region	Localized ripples and wave patterns

Comprehensive Ecosystem Components

Data Processing Pipeline

Located in /data_pipeline, this system enables:

• Automated scraping of animation sources
• Intelligent deduplication and quality filtering
• Style-consistent dataset augmentation
• Efficient preprocessing for model training

Evaluation Reward System

The /reward directory contains:

• Anime-specific quality assessment models
• Human preference alignment mechanisms
• Frame consistency evaluators
• Motion naturalness metrics

Benchmark Dataset

The project includes 948 curated animation clips featuring:

• 150+ distinct action categories
• 10-30 samples per action type
• Qwen-VL2 generated + human-validated prompts
• Balanced representation across animation styles

Future Development Trajectory

Near-Term Objectives

• May 2025: Release of 14B parameter V2.0 implementation
• June 2025: Public access to curated training datasets
• July 2025: SIGGRAPH preview of V3 architecture

Strategic Vision

1. Cross-Style Transfer: Seamless conversion between anime art styles
2. Extended Sequence Generation: Multi-scene narrative capabilities
3. Real-Time Rendering: Sub-second generation latency
4. Community Platform: Creator ecosystem with asset sharing

Access and Implementation Resources

Official Distribution Channels

Platform	Resource Type	Access Link
GitHub	Source code, documentation	https://github.com/bilibili/Index-anisora
Hugging Face	Pretrained models	https://huggingface.co/IndexTeam/Index-anisora
ModelScope	Chinese-language resources	https://www.modelscope.cn/organization/bilibili-index

Research Citation

@article{jiang2024anisora,
  title={AniSora: Exploring the Frontiers of Animation Video Generation in the Sora Era},
  author={Yudong Jiang, Baohan Xu, Siqian Yang, Mingyu Yin, Jing Liu, Chao Xu, Siqi Wang, Yidi Wu, Bingwen Zhu, Xinwen Zhang, Xingyu Zheng, Jixuan Xu, Yue Zhang, Jinlong Hou and Huyang Sun},
  journal={arXiv preprint arXiv:2412.10255},
  year={2024}
}

Conclusion: Democratizing Animation Production

Index-AniSora represents a quantum leap in accessible animation technology. By open-sourcing this sophisticated framework, Bilibili has effectively democratized tools previously accessible only to well-funded studios. The model’s specialized architecture addresses longstanding challenges in character consistency, motion fluidity, and style authenticity that generic video generation models struggle with.

For independent creators, this technology eliminates traditional barriers to animation production. For studios, it offers powerful augmentation to existing pipelines. For researchers, it provides a robust foundation for further innovation in specialized media generation.

As the project continues evolving through community collaboration and research advancement, AniSora promises to fundamentally transform how anime content is created, making high-quality animation production accessible to creators at all skill levels worldwide.