VOID Video Inpainting

VOID Video Inpainting — Object Removal

VOID Video Inpainting removes objects or people from video footage and fills the background with realistic, temporally consistent content. Describe what to remove and what the background should look like — the model handles the rest, with optional mask video input for precise control.

Why Choose This?

• Text-driven object removal Describe the object or person to remove in plain language — no manual masking required. The model uses SAM-3 to auto-generate a mask from your text description.

• Custom mask video support Upload a pre-prepared VOID-style quadmask or simple binary mask video for precise, frame-accurate removal control.

• Background inpainting Describe the desired background after removal — the model fills the gap with contextually appropriate, motion-consistent content.

• Pass 2 refinement Enable enable_pass2_refinement for additional warped-noise refinement that improves temporal consistency on longer clips.

• Fine-grained generation control Adjust inference steps, guidance scale, denoising strength, and temporal window size for precise output control.

Parameters

Parameter	Required	Description
video	Yes	Input video containing the object to remove (URL).
prompt	Yes	Text description of the desired background after object removal.
mask_video	No	Mask video URL. Supports VOID quadmask (4 grayscale values) or simple binary mask. Auto-generated if omitted.
mask_prompt	No	Text description of what to mask/remove. Used to auto-generate a mask when mask_video is not provided.
enable_pass2_refinement	No	Run Pass 2 warped-noise refinement for improved temporal consistency. Slower but higher quality. Default: false.
negative_prompt	No	Negative prompt to guide generation away from undesired outputs.
num_inference_steps	No	Number of denoising steps. Range: 1–50. Default: 30. Higher = better quality, slower.
guidance_scale	No	Classifier-free guidance scale. Range: 0–20. Default: 1.
strength	No	Denoising strength. Range: 0–1. Default: 1 (full denoising).
num_frames	No	Temporal window size. Valid values: 69, 77, 85, …, 197. Default: 85.
seed	No	Random seed for reproducible results.

Mask Video Format

The mask_video supports two formats:

• VOID quadmask (recommended): 4 grayscale values — 0 = object to remove, 63 = overlap region, 127 = affected area, 255 = background to keep.
• Simple binary mask: 0 = remove, 255 = keep.

If mask_video is not provided, a mask is auto-generated from mask_prompt using SAM-3.

How to Use

1. Upload your video — provide the source clip containing the object to remove.
2. Write your prompt — describe what the background should look like after the object is removed.
3. Provide mask input — either upload a mask_video for precise control, or provide a mask_prompt to auto-generate the mask.
4. Enable Pass 2 (optional) — check enable_pass2_refinement for improved temporal consistency on longer clips.
5. Adjust generation settings (optional) — tune inference steps, guidance scale, strength, and num_frames as needed.
6. Add negative prompt (optional) — specify elements to avoid in the inpainted output.
7. Set seed (optional) — fix the seed to reproduce a specific result.
8. Submit — generate, preview, and download your object-removed video.

Pricing

Pass 2 Refinement	Mask Video	Cost
No	No (auto)	$0.05
Yes	No (auto)	$0.10
No	Yes	$0.10
Yes	Yes	$0.15

Billing Rules

• Base cost: $0.05 (without Pass 2)
• Pass 2 surcharge: ×2 base cost when enabled
• Mask video surcharge: +$0.05 when a mask_video is provided

Best Use Cases

• Film & video post-production — Remove unwanted objects, crew members, or equipment from footage.
• Social media content — Clean up backgrounds by removing distracting elements before publishing.
• Product video cleanup — Remove staging props, logos, or unwanted foreground elements from product footage.
• Content repurposing — Strip specific elements from existing footage to repurpose clips for new contexts.

Pro Tips

• Provide a mask_video for the most accurate, frame-precise removal — especially for fast-moving or partially occluded subjects.
• If using mask_prompt for auto-generation, be specific about the object to remove (e.g. "the person on the left" rather than just "person").
• Write a detailed background prompt describing texture, lighting, and environment for more coherent fill results.
• Enable Pass 2 refinement for clips longer than a few seconds where temporal consistency matters most.
• Use a fixed seed when iterating on prompt or mask changes to isolate the effect of each adjustment.

Notes

• Both video and prompt are required fields; all other parameters are optional.
• If mask_video is omitted, mask_prompt should be provided to guide automatic mask generation.
• Valid num_frames values are: 69, 77, 85, 93, 101 … up to 197 (increments of 8 after 85).
• Ensure video and mask_video URLs are publicly accessible.
• mask_video or mask_prompt must chose one to input.