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SCAIL enables high-fidelity character animation using reference images. It handles large motion variations, stylized characters, and multi-character interactions without explicit per-frame structural guidance. Ready-to-use REST inference API, no coldstarts, affordable pricing.

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README

SCAIL — Studio-Grade Character Animation

SCAIL (Studio-Grade Character Animation with In-context Learning) is an open-source framework enabling high-fidelity character animation across diverse and challenging conditions. Unlike traditional methods, it handles large motion variations, stylized characters, and multi-character interactions without explicit per-frame structural guidance.

Key Features

  • Diverse Character Support Works with realistic humans, stylized characters, and anime — despite minimal anime training data.

  • Large Motion Handling Handles challenging motions like flipping and turning that break traditional pose-based methods.

  • Multi-Character Interactions Performs spatiotemporal reasoning across entire motion sequences for complex multi-character scenes.

  • Identity Preservation Uses 3D-consistent pose representations to prevent identity leakage while retaining rich motion details.

  • Easy Setup Works with a single reference image and a driving video — no complex preprocessing required.

Pricing

Billing duration: Input media duration is rounded up to whole seconds. The minimum billed duration is 3 seconds; shorter inputs are billed as 3 seconds. Existing per-5-second rate tables remain unchanged.

ResolutionPrice per 5sPrice per secondMax Length
480p$0.20$0.04 / s120 s
720p$0.40$0.08 / s120 s

Billing Rules

  • Minimum charge: 3 seconds — any video shorter than 3 seconds is billed as 3 seconds.
  • Maximum billed duration: 120 seconds (2 minutes)

How to Use

  1. Upload image — A clear reference image of the character (recommended formats: JPG / PNG, avoid WEBP).
  2. Upload video — The motion source; SCAIL extracts pose and motion dynamics from this clip.
  3. Add prompt (optional) — Guide the output with descriptive text. Long, detailed prompts work best.
  4. Select resolution — Choose between 480p or 720p.
  5. Generate — Wait while SCAIL processes the animation.
  6. Review & Iterate — Fix a seed to reproduce results, or vary it for different outputs.

Tips for Best Results

  • Use Detailed Prompts: SCAIL benefits from long, descriptive prompts for optimal results.
  • Match Pose & Composition: Keep your reference image's camera angle and framing close to the target video.
  • Keep Aspect Ratios Consistent: Use the same aspect ratio between your input image and video.
  • Experiment with Stylized Content: The model handles anime and stylized characters surprisingly well.
  • Multi-Character Scenes: SCAIL can handle complex multi-character interactions.
注記:本サイトは第三者が提供するAIモデルを使用しています。ドキュメントの価格は参考情報であり、最新でない場合があります。Generateボタンは見積額を表示し、最終的にはタスクの実際の請求額が適用されます。

Scail API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/scail 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 Scail below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "resolution": "480p"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/scail" \
  -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="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"

# 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' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/scail";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "resolution": "480p"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
  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 = {
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "resolution": "480p"
}

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/wavespeed-ai/scail", 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 = 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", "deleted"}:
        raise RuntimeError(result)
    time.sleep(2)

Scail API — Frequently asked questions

What is the Scail API?

Scail is a WaveSpeedAI model for pose / motion driven video, exposed as a REST API on WaveSpeedAI. SCAIL enables high-fidelity character animation using reference images. It handles large motion variations, stylized characters, and multi-character interactions without explicit per-frame structural guidance. Ready-to-use REST inference API, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Scail 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 Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/scail.

How much does Scail cost per run?

Scail starts at $0.2 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 Scail accept?

Key inputs: `prompt`, `image`, `video`, `resolution`, `seed`. 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/wavespeed-ai/scail.

How long does Scail take to generate?

Reported generation time on WaveSpeedAI is around 177 seconds per request. This is an estimate, not a latency guarantee; queue time and input settings can change the total wait. live status is visible in the prediction record.

Can I use Scail outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.

Scail | AI Motion Control Video API on WaveSpeedAI