Who should use the Character Rigging workflow?
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Work
Practical execution plan for character rigging with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A finalized final deliverable is ready for publishing, handoff, or integration.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A finalized final deliverable is ready for publishing, handoff, or integration.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Simplified AI Image Generator to inputs, context, and settings are ready so the workflow can move into execution without blockers. Then, you pass the output to Places365 to supporting assets from semantic segmentation are prepared and connected to the main workflow. Then, you pass the output to Reface to supporting assets from face swapping are prepared and connected to the main workflow. Then, you pass the output to Flair AI to the final deliverable is improved, validated, and prepared for final delivery. Finally, Ephesoft (by Tungsten Automation) is used to a finalized final deliverable is ready for publishing, handoff, or integration.
Text-to-Image
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
Semantic Segmentation
Supporting assets from semantic segmentation are prepared and connected to the main workflow.
Face Swapping
Supporting assets from face swapping are prepared and connected to the main workflow.
Background Replacement
The final deliverable is improved, validated, and prepared for final delivery.
OCR
A finalized final deliverable is ready for publishing, handoff, or integration.
Prepare inputs and settings through Text-to-Image before running character rigging.
Text-to-Image sets up the foundation for character rigging; clean inputs here reduce downstream rework.
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
Use Semantic Segmentation to build supporting assets that improve character rigging quality.
Semantic Segmentation strengthens character rigging by feeding better supporting material into the pipeline.
Supporting assets from semantic segmentation are prepared and connected to the main workflow.
Use Face Swapping to build supporting assets that improve character rigging quality.
Face Swapping strengthens character rigging by feeding better supporting material into the pipeline.
Supporting assets from face swapping are prepared and connected to the main workflow.
Refine and validate character rigging output using Background Replacement before final delivery.
Background Replacement adds quality control so issues are caught before the workflow is finalized.
The final deliverable is improved, validated, and prepared for final delivery.
Package and ship the output through OCR so character rigging reaches end users.
OCR is what turns intermediate output into a usable, publishable result for real users.
A finalized final deliverable is ready for publishing, handoff, or integration.
Timeline Map
§ Before you start
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
§ Related
End-to-end workflow to monitor data pipelines, detect anomalies, define quality rules, and generate executive trust metrics using DQLabs' AI-native platform.
A workflow to discover academic literature by exploring citation networks using Inciteful, identify seminal works and emerging fronts, and compile a literature review starting point.