Animate images with Google Veo 3.1 API. Turn still visuals into realistic videos with consistent motion, native audio, and stable output.
Google Veo 3.1 Image-to-Video API delivers production-grade AI video animation for developers and creative teams. This premium video animation API integration helps you transform static images into cinematic motion through natural-language instructions. Built on Google DeepMind's cutting-edge video synthesis research, the Veo 3.1 model provides advanced motion understanding, while the API provides stable integration for scalable workflows on Best Image AI.
Note Please ensure your prompts comply with Google's Safety Guidelines. If an error occurs, review your prompt for restricted content, adjust it, and try again.
Veo 3.1 Image-to-Video vs. Runway Gen-3 Image-to-Video
Runway Gen-3 offers strong creative flexibility and rapid iteration. Veo 3.1 Image-to-Video API distinguishes itself with superior temporal consistency, advanced frame control options, and better preservation of input image style and quality through Google DeepMind's advanced video synthesis technology.
Veo 3.1 Image-to-Video vs. Pika Image-to-Video
Pika excels at stylized animations and ease of use. Veo 3.1 Image-to-Video API is optimized for production-grade quality with photorealistic motion, professional cinematography controls, and precise start/end frame guidance for predictable animation outcomes.
Veo 3.1 Image-to-Video vs. Kling Image-to-Video
Kling is strong in character animation and human motion. Veo 3.1 Image-to-Video API counters with broader scene understanding, superior environmental dynamics, and more sophisticated camera control options, making it versatile for diverse animation scenarios from product demos to narrative storytelling.
Veo 3.1 Image-to-Video vs. Luma Dream Machine
Luma Dream Machine is celebrated for rapid generation speeds. Veo 3.1 Image-to-Video API trades speed for significantly higher quality, offering advanced motion coherence and granular frame control for professional animation workflows through premium Google API integration.
Veo 3.1 Image-to-Video vs. Traditional Animation Software
Traditional software requires manual keyframing and extensive technical expertise. Veo 3.1 Image-to-Video API automates complex animation and camera movements based on natural language prompts, drastically reducing production time while maintaining professional quality for immediate deployment.
// Step 1: Submit generation request
const response = await fetch('https://api.flaq.ai/api/v1/video/task', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
body: JSON.stringify({
model_name: 'veo3.1-image-to-video',
prompt: 'The camera slowly zooms in on the subject',
aspect_ratio: '16:9',
image_url: 'https://example.com/start-frame.jpg',
image_end_url: 'https://example.com/end-frame.jpg'
})
});
const { data } = await response.json();
const taskId = data.task_id;
// Step 2: Poll for results
const taskId = data.task_id;
const pollResult = async (taskId) => {
const res = await fetch(`https://api.flaq.ai/api/v1/video/${taskId}`, {
headers: { 'Authorization': 'Bearer YOUR_API_KEY' }
});
return res.json();
};
while (true) {
const pollResultData = await pollResult(taskId);
const status = pollResultData.data.task_status;
if (status === 'succeed') {
console.log(pollResultData.data.task_result.videos[].);
;
}
(status === ) {
.(pollResultData..);
;
}
( (resolve, ));
}
# Step 1: Submit generation request
import requests
response = requests.post(
'https://api.flaq.ai/api/v1/video/task',
headers={
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
json={
'model_name': 'veo3.1-image-to-video',
'prompt': 'The camera slowly zooms in on the subject',
'aspect_ratio': '16:9',
'image_url': 'https://example.com/start-frame.jpg',
'image_end_url': 'https://example.com/end-frame.jpg'
}
)
result = response.json()
task_id = result['data']['task_id']
# Step 1: Submit generation request
curl -X POST https://api.flaq.ai/api/v1/video/task \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model_name": "veo3.1-image-to-video",
"prompt": "The camera slowly zooms in on the subject",
"aspect_ratio": "16:9",
"image_url": "https://example.com/start-frame.jpg",
"image_end_url": "https://example.com/end-frame.jpg"
}'
# Step 2: Poll for results
# Replace {task_id} with the task_id returned from the submit response
curl -X GET "https://api.flaq.ai/api/v1/video/{task_id}" \
-H "Authorization: Bearer YOUR_API_KEY"
# Step 2: Poll for results
task_id = response.json()['data']['task_id']
poll_url = f"https://api.flaq.ai/api/v1/video/{task_id}"
while True:
poll_result = requests.get(poll_url, headers={'Authorization': 'Bearer YOUR_API_KEY'}).json()
status = poll_result['data']['task_status']
if status == 'succeed':
print(poll_result['data']['task_result']['videos'][0]['url'])
break
if status == 'failed':
print(poll_result['data']['task_status_msg'])
break
time.sleep(10)