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Bria Video Eraser (Mask) removes unwanted objects from videos using a user-provided mask video. Mark regions frame-by-frame (black/white or alpha), and the model performs AI video inpainting to reconstruct clean, temporally consistent backgrounds for people, logos, text, and props. Ready-to-use REST API with fast response, best performance, no cold starts, and affordable pricing.

ai-remover
Input

Idle

$0.05per run·~20 / $1

ExamplesView all

Related Models

README

Bria Video Eraser (Mask-Based)

Bria Video Eraser (Mask-Based) is a precision video inpainting tool that removes objects using a mask video. Provide the original video plus a matching mask video (white = erase, black = keep), and the model removes the masked regions frame-by-frame while reconstructing the background for clean, production-ready results.

This mode is ideal for creators and post-production teams who need pixel-accurate control over what gets removed—without manual frame-by-frame painting.

Why it stands out

  • Mask-based control for precise, frame-consistent object removal.
  • Clean inpainting to reconstruct backgrounds after erasing masked regions.
  • Works well for VFX cleanup, unwanted object removal, and scene polishing.
  • copy_audio toggle to preserve or remove the original audio track.
  • Transparent, duration-based pricing based on BRIA official per-second rates.

Capabilities

  • Mask-video guided object removal (white regions removed, black regions preserved)
  • Background reconstruction (video inpainting) across frames
  • Frame-by-frame precision for moving objects and tracked masks
  • Optional original audio preservation via copy_audio

Parameters

ParameterDescription
video*Input video file or public URL.
mask_video*Mask video defining erase vs keep (white = remove, black = keep). Must align with the input video.
copy_audioWhether to keep the original audio in the output video (true = preserve, false = remove).

How to use

  1. Upload the video you want to edit (or paste a public URL).
  2. Upload a matching mask_video:
  • White areas = regions to erase
  • Black areas = regions to keep
  1. Make sure the mask video matches the input video’s resolution, duration, and timing for best results.
  2. Choose copy_audio:
  • true to preserve the original audio track
  • false to output a silent video
  1. Run the model, preview the output, and refine the mask if needed.

Mask tips (best practices)

  • Use pure white for removal and pure black for preservation whenever possible.
  • Slight edge feathering can reduce hard seams and improve blending.
  • If the mask “misses” parts of the object during motion, expand the mask slightly and re-run.
  • For complex motion, generate masks with rotoscoping tools or segmentation models, then export as a mask video.

Pricing

Pricing is $0.05 per second of input video duration.

Current endpoint supports videos up to 5 seconds per request.

Video DurationPrice
1s$0.05
2s$0.10
3s$0.15
4s$0.20
5s$0.25

Notes

  • The mask video must be properly aligned; misaligned masks can cause jittery edges or incomplete removals.
  • Fast motion blur and heavy occlusion may reduce inpainting quality—use tighter masks and short test clips first.
  • If you need quick removal without mask prep, use the prompt-based variant instead.
  • If you want to generate a mask video. Please use the SAM-3 Video to build one!

Related Models

Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Video Eraser Mask API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bria/video-eraser/mask with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Video Eraser Mask below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "mask_video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "copy_audio": true
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/bria/video-eraser/mask" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/bria/video-eraser/mask";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "mask_video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "copy_audio": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "mask_video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "copy_audio": True
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/bria/video-eraser/mask", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Video Eraser Mask API — Frequently asked questions

What is the Video Eraser Mask API?

Video Eraser Mask is a Bria model for object / watermark removal, exposed as a REST API on WaveSpeedAI. Bria Video Eraser (Mask) removes unwanted objects from videos using a user-provided mask video. Mark regions frame-by-frame (black/white or alpha), and the model performs AI video inpainting to reconstruct clean, temporally consistent backgrounds for people, logos, text, and props. Ready-to-use REST API with fast response, best performance, no cold starts, and affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Video Eraser Mask API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/bria/bria-video-eraser-mask.

How much does Video Eraser Mask cost per run?

Video Eraser Mask starts at $0.050 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Video Eraser Mask accept?

Key inputs: `video`, `copy_audio`, `mask_video`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/bria/bria-video-eraser-mask.

How long does Video Eraser Mask take to generate?

Median end-to-end generation time on WaveSpeedAI is around 217 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Video Eraser Mask outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Bria). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Video Eraser Mask | AI Background & Object Remover API on WaveSpeedAI