How Seedance 2.0 Handles Subtitles and Localization for Video: A Reference-Driven Workflow

Seedance 2.0 treats subtitles and localization as post-production deliverables, using a reference-driven architecture that separates clean AI-generated plates from language-specific assets produced downstream.

Seedance 2.0 is an open-source video generation framework that enforces strict separation between AI-generated content and final localization assets. Rather than burning text into generated frames, the system produces clean, textless plates alongside structured metadata that drives post-production subtitle pipelines. This approach ensures professional delivery standards while supporting complex localization requirements including SDH captions, forced narratives, and script variants.

The Reference-Driven Architecture

At the core of Seedance 2.0's localization strategy is the references/subtitles-localization.md file. This document defines the complete subtitle pipeline as a post-production workflow rather than a generation-time feature.

The architecture mandates six distinct asset types handled separately from the AI generation:

• Subtitles – Translated dialogue authored from approved scripts in post-production
• SDH captions – Sound-cue information for accessibility, added during editing
• Forced narrative – Translations of on-screen signs or foreign dialogue, never burned into frames
• Dubbing – Alternative audio tracks
• M&E (Music and Effects) – Textless audio stems
• Textless plates – Clean versions of the footage without titles or graphics

This separation ensures that the AI model focuses exclusively on generating high-quality visual content while specialized post-production tools handle linguistic accuracy and compliance.

Subtitle Plan Schema Integration

The project configuration schema in references/json-schema.md includes a dedicated subtitle_plan object that records client localization requirements during project setup. This schema validates the downstream pipeline configuration before generation begins.

The subtitle_plan object supports the following fields:

{
  "subtitle_plan": {
    "subtitles": true,
    "sdh": false,
    "languages": ["es", "ja", "ko", "ru", "zh-Hant"],
    "reading_time_ms": 2500,
    "placement": "lower-third-safe"
  }
}

Validation occurs during project initialization, ensuring the pipeline knows exactly which assets to produce and which quality-control checks to apply. The placement field specifically guides the composition engine to maintain caption-safe areas.

Prompt-Time Guidance for Subtitle-Friendly Footage

The subtitles-localization.md reference instructs the generation model to create footage optimized for downstream captioning. Rather than generating text on screen, the model follows specific cinematographic guidelines:

• Keep dialogue short and assign clear speaker tags
• Use stable medium close-up shots to minimize motion vector complexity for caption placement
• Leave negative space in the lower third for caption insertion
• Avoid generating burnt-in text, signs, or foreign language elements that would require invasive editing

This guidance ensures that generated plates require minimal intervention before subtitle attachment.

Handling Localization Variants

For languages with multiple script variants, Seedance 2.0 prevents naive character substitution errors through explicit vocabulary management. The skills/seedance-vocab-zh/SKILL.md file demonstrates this by separating Simplified Chinese (zh-Hans) from Traditional Chinese (zh-Hant).

The vocab file declares delivery scripts separately from prompt scripts, preventing errors such as converting 头发 (hair) to 頭髮 (Traditional) when the correct Traditional variant for "hair" is actually 頭發 in specific regional contexts. This forces the pipeline to generate distinct subtitle files for each script variant rather than attempting automatic conversion of generated content.

Quality Control and Validation

The references/delivery-qc.md reference provides platform-specific quality control checks. This includes verification that footage maintains caption-safe framing according to standards like Netflix Timed Text specifications and WebVTT accessibility rules.

Additionally, the evaluation suite in evals/evals.json includes a dedicated subtitle_localization_accessibility test. This test confirms that the model proposes a localization plan rather than generating final subtitles, enforcing the architectural boundary between generation and post-production.

Implementation Examples

The following snippets illustrate how Seedance 2.0 integrates subtitle planning into the generation pipeline:

Pipeline Skill Declaration

In skills/seedance-pipeline/SKILL.md, the subtitle reference is loaded as part of the post-workflow definition:

4. Post workflow: edit, conform, stitching, stabilization, audio cleanup,
   captions/subtitles, color, localization, versioning, textless, and delivery.

This line automatically pulls in subtitles-localization.md and makes its guidelines available to the generation engine.

Prompt Generation Helper

The system uses helper functions to enforce subtitle-friendly prompting:

def make_subtitle_friendly_prompt(dialogue, speaker):
    # Keep sentences short, assign speaker tag, request stable framing

    return f'''
    {speaker} says: "{dialogue}"
    Shot: medium close‑up, keep background clean, leave space at bottom for captions.
    '''

Post-Production Command Interface

After generation, subtitle assets are produced from the approved script:

seedance generate‑subtitles --script generated_script.txt \
    --languages es ja ko ru zh-Hant \
    --format webvtt --output ./subtitles/

Summary

• Seedance 2.0 enforces a strict separation between AI-generated footage and subtitle assets through the subtitles-localization.md reference architecture.
• The subtitle_plan object in json-schema.md validates localization requirements before generation begins, specifying languages, reading times, and safe placement zones.
• The generation model creates textless plates with subtitle-friendly framing (stable shots, negative space) rather than embedding text.
• Script variants (like zh-Hans vs zh-Hant) are handled through separate vocabulary files to prevent translation errors.
• Quality control in delivery-qc.md ensures platform compliance (Netflix, WebVTT) and caption-safe framing.
• The subtitle_localization_accessibility evaluation test verifies that the system plans subtitles rather than generating them on-screen.

Frequently Asked Questions

Does Seedance 2.0 burn subtitles into generated video frames?

No. Seedance 2.0 explicitly prohibits burning subtitles into AI-generated frames. According to references/subtitles-localization.md, subtitles are authored in post-production from approved scripts and attached as separate SRT or WebVTT files. The model is instructed to leave space for captions but never to generate text on screen.

How does Seedance 2.0 handle Chinese language variants in subtitles?

The system handles Simplified (zh-Hans) and Traditional (zh-Hant) Chinese through separate vocabulary declarations in skills/seedance-vocab-zh/SKILL.md. This prevents naive character conversion errors by treating each script as a distinct delivery target with its own subtitle file, ensuring regional linguistic accuracy.

What is the purpose of the subtitle_plan object in the JSON schema?

The subtitle_plan object in references/json-schema.md records client localization choices during project setup. It specifies which languages require subtitles, whether SDH captions are needed, target reading speeds (reading_time_ms), and safe placement zones. This schema is validated before generation to configure the downstream pipeline correctly.

How does Seedance 2.0 ensure subtitle-safe framing in generated footage?

The subtitles-localization.md reference provides prompt-time guidance requesting medium close-up shots with stable backgrounds and negative space in the lower third. Additionally, references/delivery-qc.md includes automated checks for caption-safe framing before final export, ensuring compliance with platform-specific safe zones.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}