Image Background

Workflow

1. Upload your background image and get a URL.
2. Create job with bg_type=image and bg_image_url.
3. Start and poll status until completed.

Best Practices

• Match output aspect ratio with source video.
• Use high-resolution backgrounds to avoid blur.
• Add light/shadow matching in downstream composition if needed.

Quality Guardrails

• Prefer representative source clips when tuning model defaults.
• Keep input compression moderate to preserve edge details.
• Use explicit output format policy for transparent vs non-transparent workflows.
• Run A/B validation with the same clip set before changing defaults.

Implementation Checklist

1. Define payload schema validation in backend before forwarding requests.
2. Store model/output_format/background settings with each job record.
3. Add internal quality review for difficult scenes (hair, glass, motion blur).
4. Create runbook entries for model-specific failure cases.

Background Image Performance

When to Use Image Background

Image Background belongs to the Guides section and covers use a custom background image in final output.

The page is written for developers and operators who need predictable video background removal behavior in production, not just a one-off demo request.

• Use this guide when image background decisions affect output quality, processing cost, or user-facing workflow design.
• Test guidance on representative videos rather than only short demo clips.
• Record the chosen model, background, output format, and quality notes with each processed job for later debugging.

Implementation Notes

Before you promote this workflow, test it with at least one short clip, one longer clip, and one visually difficult clip from your actual product or customer segment.

For support and debugging, persist the original input reference, selected model, output format, credit usage, and final job status alongside your internal user or project id.

• Do not judge quality from a single frame; inspect motion, hair, hands, transparent edges, and fast turns.
• Do not compare model outputs using different source compression levels.
• Do not change production defaults without a rollback path and a known sample set.

Related Documentation

FAQ