Motion Controls

Motion Controls

• Motion Controls is an AI-powered video effect system that dynamically animates static images or enhances existing videos with motion-driven transformations such as zoom, spin, shake, bounce, and custom camera path effects.

Features

• Motion Controls enables fluid animation effects, simulating camera movements and object dynamics using AI-generated motion layers and keyframes.

Key Features

• Dynamic Motion Effects: Apply zoom, spin, shake, pan, rotate, and bounce using prompt-based or preset controls.
• Static-to-Motion Animation: CTurn static images into moving visuals with lifelike camera movement and animation flows.
• Smooth Transitions: Generates interpolated frames for consistent, flicker-free animation.
• Prompt or Preset-Based: Supports both natural language prompts (e.g., “rotate left and zoom in”) and predefined effect types.
• Short Clip Focus: Optimized for clips between 5–10 seconds for maximum quality.
• Fast Inference Pipeline: Most clips render in under 2 minutes depending on duration and resolution.

Limitations

• Not Real-World Motion Capture: Does not track or mimic real object motion — it creates synthetic camera/path-based motion.
• Short Clip Optimization: Not suitable for clips longer than 10 seconds without segmentation.
• Prompt Ambiguity: Prompts that lack specificity (e.g., “move it around”) may lead to unexpected output.
• Single Subject Focus: Works best on images/videos with one main subject or focal point.

Out-of-Scope Use

• Motion Controls must not be used in ways that violate ethical guidelines or legal standards, including:

  1. Generating misleading content to impersonate real-world footage.
  2. Applying motion to sensitive, violent, or harmful imagery.
  3. Enhancing or animating NSFW content.
  4. Harassment or abuse via manipulated video motion effects.
  5. Using AI-generated animations for political or misinformation campaigns.

Authentication

• For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

• cURL

  # Submit Task
  curl --location --request POST 'https://api.muapi.ai/api/v1/  generate_wan_ai_effects' \
    --header "Content-Type: application/json" \
    --header "x-api-key: {MUAPIAPP_API_KEY}" \
    --data-raw '{
      "prompt": "a blueberry person",
      "image_url": "https://d3adwkbyhxyrtq.cloudfront.net/ai-images/186/833006366055/4f966d88-9ad7-4bd9-966d-c0fd7c5a9eb9.jpg",
      "name": "360 Orbit",
      "aspect_ratio": "16:9",
      "resolution": "480p",
      "quality": "medium",
      "duration": 5
    }'
  
  # Poll for results
  curl --location --request GET "https://api.muapi.ai/api/v1/predictions/$  {request_id}/result" \
    --header "x-api-key: {MUAPIAPP_API_KEY}"
  

• Python

  import os
  import requests
  import json
  import time
  
  from dotenv import load_dotenv
  load_dotenv()
  
  def main():
    print("Hello from MuApiApp!")
    API_KEY = os.getenv("MUAPIAPP_API_KEY")
    print(f"API_KEY: {API_KEY}")
  
    url = "https://api.muapi.ai/api/v1/generate_wan_ai_effects"
    headers = {
      "Content-Type": "application/json",
      "x-api-key": f"{API_KEY}",
    }
    payload = {
      "prompt": "a blueberry person",
      "image_url": "https://d3adwkbyhxyrtq.cloudfront.net/ai-images/186/833006366055/4f966d88-9ad7-4bd9-966d-c0fd7c5a9eb9.jpg",
      "name": "360 Orbit",
      "aspect_ratio": "16:9",
      "resolution": "480p",
      "quality": "medium",
      "duration": 5
    }
  
    begin = time.time()
    response = requests.post(url, headers=headers, data=json.dumps(payload))
    if response.status_code == 200:
      result = response.json()
      request_id = result["data"]["request_id"]
      print(f"Task submitted successfully. Request ID: {request_id}")
    else:
      print(f"Error: {response.status_code}, {response.text}")
      return
  
    url = f"https://api.muapi.ai/api/v1/predictions/{request_id}/result"
    headers = {"x-api-key": f"{API_KEY}"}
  
    while True:
      response = requests.get(url, headers=headers)
      if response.status_code == 200:
        result = response.json()
        status = result["data"]["status"]
  
        if status == "completed":
          end = time.time()
          print(f"Task completed in {end - begin} seconds.")
          url = result["video"]["url"]
          print(f"Task completed. URL: {url}")
          break
        elif status == "failed":
          print(f"Task failed: {result.get('error')}")
          break
        else:
          print(f"Task still processing. Status: {status}")
      else:
        print(f"Error: {response.status_code}, {response.text}")
        break
  
      time.sleep(0.5)
  
  if __name__ == "__main__":
    main()
  

• JavaScript

  require('dotenv').config();
  const axios = require('axios');
  
  async function main() {
    console.log("Hello from MuApiApp!");
  
    const API_KEY = process.env.MUAPIAPP_API_KEY;
    console.log(`API_KEY: ${API_KEY}`);
  
    const url = 'https://api.muapi.ai/api/v1/generate_wan_ai_effects';
    const headers = {
      'Content-Type': 'application/json',
      'x-api-key': `${API_KEY}`,
    };
  
    const payload = {
      prompt: 'a blueberry person',
      image_url: 'https://d3adwkbyhxyrtq.cloudfront.net/ai-images/186/833006366055/4f966d88-9ad7-4bd9-966d-c0fd7c5a9eb9.jpg',
      name: '360 Orbit',
      aspect_ratio: '16:9',
      resolution: '480p',
      quality: 'medium',
      duration: 5,
    };
  
    try {
      const begin = Date.now();
      const response = await axios.post(url, payload, { headers });
  
      if (response.status === 200) {
        const requestId = response.data.data.request_id;
        console.log(`Task submitted successfully. Request ID: ${requestId}`);
  
        const resultUrl = `https://api.muapi.ai/api/v1/predictions/${request_id}/result`;
        const pollHeaders = { x-api-key: `${API_KEY}` };
  
        while (true) {
          const pollResponse = await axios.get(resultUrl, { headers: pollHeaders });
  
          if (pollResponse.status === 200) {
            const status = pollResponse.data.data.status;
  
            if (status === 'completed') {
              const end = Date.now();
              console.log(`Task completed in ${(end - begin) / 1000} seconds.`);
              const videoUrl = pollResponse.data.video.url;
              console.log(`Task completed. URL: ${videoUrl}`);
              break;
            } else if (status === 'failed') {
              console.log(`Task failed: ${pollResponse.data.error}`);
              break;
            } else {
              console.log(`Task still processing. Status: ${status}`);
            }
          } else {
            console.log(`Error: ${pollResponse.status}, ${pollResponse.statusText}`);
            break;
          }
  
          await new Promise(resolve => setTimeout(resolve, 500));
        }
      } else {
        console.log(`Error: ${response.status}, ${response.statusText}`);
      }
    } catch (error) {
      console.error(`Request failed: ${error.message}`);
    }
  }
  
  main();
  

Parameters

Task Submission Parameters

Request Parameters

Parameter	Type	Required	Default	Range	Description
prompt	string	Yes	""	-	The prompt for generating the output.
image_url	string	Yes	""	-	The image for generating the output
name	string	Yes	""	-	Custom name for the task or output file.
aspect_ratio	string	No	16:9	1:1, 9:16, 16:9	Aspect ratio of the output video.
resolution	string	No	480p	480p, 720p	Resolution of the output video.
quality	string	No	medium	medium, high	Controls overall video quality (affects rendering time).
duration	number	No	5	5 ~ 10	Length of the generated video in seconds.

Response Parameters

Parameter	Type	Description
code	integer	HTTP status code (e.g., 200 for success)
message	string	Status message (e.g., “success”)
id	string	Unique identifier for the generation task.
outputs	array of strings	URLs pointing to the generated video output(s).
urls.get	string	API URL to retrieve the result by task ID.
has_nsfw_contents	array of booleans	Indicates whether the output contains NSFW content.
status	string	Status of the task: completed, failed, or processing.
created_at	string (timestamp)	ISO timestamp indicating when the task was created.
error	string	Error message if the task failed; empty if successful.
executionTime	number (milliseconds)	Total time taken to complete the task in milliseconds.
timings.inference	number (milliseconds)	Time taken specifically for model inference in milliseconds.

Result Query Parameters

Result Request Parameters

Parameter	Type	Required	Default	Description
id	string	Yes	-	Task ID

Result Response Parameters

Parameter	Type	Description
code	integer	HTTP status code (e.g., 200 for success)
message	string	Status message (e.g., “success”)
id	string	Unique identifier for the generation task.
outputs	array of strings	URLs pointing to the generated video output(s).
urls.get	string	API URL to retrieve the result by task ID.
has_nsfw_contents	array of booleans	Indicates whether the output contains NSFW content.
status	string	Status of the task: completed, failed, or processing.
created_at	string (timestamp)	ISO timestamp indicating when the task was created.
error	string	Error message if the task failed; empty if successful.
executionTime	number (milliseconds)	Total time taken to complete the task in milliseconds.
timings.inference	number (milliseconds)	Time taken specifically for model inference in milliseconds.