Block 1 - Validate & Extract Roto Brief
- Type / Role
- n8n-nodes-base.code - code
- Config choices
- Version 2
Description This workflow is an AI powered roto matte generation and first pass compositing pipeline designed for VFX production. It transforms structured roto requests into multiple high precis...
n8n-nodes-base.code, n8n-nodes-base.httprequest, n8n-nodes-base.wait, n8n-nodes-base.if, n8n-nodes-base.slack, n8n-nodes-base.errortrigger, n8n-nodes-base.googlesheetstrigger, n8n-nodes-base.jira
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Rahul Joshi.
Original n8n.io sourceThis workflow is an AI-powered roto matte generation and first-pass compositing pipeline designed for VFX production. It transforms structured roto requests into multiple high-precision matte passes using AI, performs automated quality evaluation, generates production-ready Nuke scripts, and delivers fully organized assets and review tasks to the team—bridging the gap between manual rotoscoping and scalable AI-assisted matte generation.
The workflow begins with a Google Sheets trigger that continuously monitors for new roto requests, acting as the intake layer for structured production data such as plate image URLs, subject descriptions, shot codes, subject type, and complexity. Once a new entry is detected, the system moves into a validation and extraction stage, where input fields are normalized, defaults are assigned, and essential attributes like sequence codes, frame ranges, QC thresholds, and project metadata are structured for downstream processing. The pipeline then performs prompt engineering and expands the request into four specialized roto passes: an alpha matte pass for clean silhouette extraction, an edge detail pass for fine hair and motion blur accuracy, a foreground isolation pass for compositing-ready subject separation, and a motion tracking reference pass enriched with tracking markers and velocity overlays. Each variation includes detailed instructions aligned with shot context and technical constraints to ensure precise AI-driven outputs.
At the core of the workflow, an image-to-video generation layer constructs structured API requests and submits each variation as an independent job to the Seedance AI model, always using the plate image as a reference for accurate subject extraction. A polling mechanism continuously monitors each job at fixed intervals, ensuring that processing proceeds only after successful completion. Once rendering is complete, a metadata and automated QC layer evaluates each pass by assigning a quality score based on resolution and predefined thresholds, classifying outputs as either AI-approved or requiring manual intervention. A decision routing layer then determines the next stage—approved passes continue through the pipeline, while failed outputs trigger immediate Slack alerts for senior artist review and manual roto assignment.
For approved outputs, the system generates a fully production-ready Nuke roto template, automatically configuring nodes for plate input, AI matte extraction, alpha application, edge refinement, and artist-level roto adjustments, along with structured EXR output paths and naming conventions. The pipeline then proceeds to asset handling, where generated videos are downloaded and distributed via email to requesters, while simultaneously creating Jira review tasks to ensure pipeline tracking and accountability. An aggregation layer consolidates all roto passes into a unified structured summary, combining QC results, video links, task references, and technical metadata. Finally, a team notification system delivers a comprehensive summary to the roto team via Telegram, including per-pass QC status, preview links, task tracking, output paths, and artist notes highlighting areas requiring refinement—ensuring seamless collaboration between AI outputs and human artists.
• Global error trigger across the workflow • Instant Slack alerts with failure details • Prevents silent failures and ensures pipeline reliability
• Seedance API (AI video generation) • Google Sheets OAuth2 (trigger input) • Jira API (task tracking) • Gmail OAuth2 (asset delivery) • Slack OAuth2 (alerts & QC failures) • Telegram Bot (team notifications)
✔ Automated roto matte generation with AI-assisted precision ✔ Multiple specialized passes for production flexibility ✔ Built-in QC scoring and intelligent decision routing ✔ Auto-generated Nuke scripts with pre-configured nodes ✔ Integrated task tracking via Jira ✔ Seamless delivery via email and team notifications ✔ Scalable pipeline for high-volume roto workflows
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
Showing the first 24 of 29 workflow blocks. Download the JSON for the full node graph.
| Workflow | Generate multi-pass Seedance AI roto mattes with QC and Nuke handoff |
|---|---|
| Complexity | advanced |
| Nodes | 29 |
| Categories | Engineering, Multimodal AI |
| Author | Rahul Joshi |
| Published | 27 Mar 2026 |
Use the JSON export at /data/workflows/14390/14390.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
Description This workflow is an AI powered roto matte generation and first pass compositing pipeline designed for VFX production. It transforms structured roto requests into multiple high precis...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, Multimodal AI use case.