# Veo3.1 Reference-to-Video — Atlas Cloud API

> Create richly detailed videos guided by visual references. Veo 3.1 Reference-to-Video preserves characters, style, and composition across scenes for consistent, visually coherent storytelling.

This is the machine-readable API reference for **Veo3.1 Reference-to-Video** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `google/veo3.1/reference-to-video`
- **Built by**: Google
- **Modality**: Video
- **Model page**: https://www.atlascloud.ai/models/google/veo3.1/reference-to-video
- **API key**: https://www.atlascloud.ai/console/api-keys
- **Docs**: https://www.atlascloud.ai/docs

## Pricing on Atlas Cloud

- $0.2 per second of generated video
- Pay-as-you-go. No minimum spend, no subscription required.

> **These are the authoritative Atlas Cloud rates for this model.** Any price that
> appears in the vendor description further down refers to a different platform or
> a different model variant and does not apply here.

## Use this model from an AI agent

Atlas Cloud ships three first-party integration surfaces. All three authenticate
with the same API key via the `ATLASCLOUD_API_KEY` environment variable.

### MCP server

The official MCP server (`atlascloud-mcp`) exposes this model to any
MCP-compatible host — Claude Code, OpenAI Codex, Cursor, Gemini CLI, Goose,
Claude Desktop. One-line install:

```bash
# Claude Code
claude mcp add atlascloud -- npx -y atlascloud-mcp

# OpenAI Codex CLI
codex mcp add atlascloud -- npx -y atlascloud-mcp

# Gemini CLI
gemini mcp add atlascloud -- npx -y atlascloud-mcp

export ATLASCLOUD_API_KEY="your-api-key"
```

Then ask in plain English; the agent calls `atlas_generate_video` with `model: "google/veo3.1/reference-to-video"`.
The server fetches each model's schema and validates parameters before submitting,
so invalid requests fail fast without spending credits.

MCP docs: https://www.atlascloud.ai/docs/mcp-server

### Agent Skills

`atlas-cloud-skills` is a portable skill package (API reference, code templates in
Python / Node.js / cURL, model IDs with pricing) for Claude Code, Cursor, Codex and
12+ other agents:

```bash
npx skills add AtlasCloudAI/atlas-cloud-skills
export ATLASCLOUD_API_KEY="your-api-key"
```

Skills docs: https://www.atlascloud.ai/docs/skills

### CLI

The `atlas` binary runs Atlas Cloud from a terminal or CI script. Async media jobs
are polled and downloaded automatically (use `--no-download` when a script only
needs the output URLs):

```bash
# Install (Homebrew, npm, or shell installer)
brew install AtlasCloudAI/tap/atlascloud
# npm install -g atlascloud-cli
# curl -fsSL https://raw.githubusercontent.com/AtlasCloudAI/cli/main/install.sh | sh

atlas auth login
atlas generate video google/veo3.1/reference-to-video -p "Your prompt here"
```

CLI docs: https://www.atlascloud.ai/docs/cli

## HTTP API reference

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateVideo` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `google/veo3.1/reference-to-video`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"google/veo3.1/reference-to-video"`

- **`prompt`** (`string`, _required_):
  The positive prompt for the generation.

- **`images`** (`array[string]`, _required_):
  The model will use the provided images as references to generate a video with consistent subjects. For fields that accept images: Accepts 1 to 3 images; Images Assets can be provided via URLs or Base64 encode; You must use one of the following codecs: PNG, JPEG, JPG, WebP; The dimensions of the images must be at least 128*128 pixels; All images are limited to 50MB; The length of the base64 decode must be under 50MB, and it must include an appropriate content type string.
  - Min items: 1
  - Max items: 3

- **`duration`** (`integer`, _optional_):
  The duration of the generated media in seconds.
  - Default: `8`
  - Options: 8

- **`resolution`** (`string`, _optional_):
  Video resolution.
  - Default: `"720p"`
  - Options: "720p", "1080p", "4k"

- **`generate_audio`** (`boolean`, _optional_):
  Whether to generate audio.
  - Default: `false`

- **`negative_prompt`** (`string`, _optional_):
  The negative prompt for the generation.

- **`seed`** (`integer`, _optional_):
  The random seed to use for the generation.



**Required Parameters Example**:

```json
{
  "model": "google/veo3.1/reference-to-video",
  "prompt": "",
  "images": [
    ""
  ]
}
```


**Full Example**:

```json
{
  "model": "google/veo3.1/reference-to-video",
  "prompt": "",
  "images": [
    ""
  ],
  "duration": 8,
  "resolution": "720p",
  "generate_audio": false,
  "negative_prompt": "",
  "seed": 0
}
```


### Output Schema

The API returns the following output format:


- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  Array of boolean values indicating NSFW detection for each output.

- **`id`** (`string`, _optional_):
  Unique identifier for the prediction, the ID of the prediction to get.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated content (empty when status is not completed).

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.



**Example Response**:

```json
{
  "created_at": "",
  "has_nsfw_contents": [],
  "id": "",
  "model": "",
  "outputs": [
    ""
  ],
  "status": "",
  "urls": {}
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "google/veo3.1/reference-to-video",
  "prompt": "",
  "images": [
    ""
  ],
  "duration": 8,
  "resolution": "720p",
  "generate_audio": false,
  "negative_prompt": "",
  "seed": 0
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/google/veo3.1/reference-to-video)

## About this model

_Vendor-supplied description. Any pricing or endpoint mentioned below refers to_
_other platforms — use the Atlas Cloud values above._

#### Google Veo 3.1 — Reference-to-Video Model

**Veo 3.1 Reference-to-Video** brings static images to life by combining **visual reference consistency** with **cinematic motion generation**. Powered by **Google DeepMind’s next-generation Veo 3.1 architecture**, this model transforms up to three reference images into coherent 5-second videos with **smooth motion**, **accurate visual alignment**, and **synchronized native audio**.

#### 🌟 Key Features

##### 🧠 Multi-Image Reference Support

-   Accepts up to **three reference images** to define the subject, environment, or style.
-   Maintains consistent identity, lighting, and appearance across frames.
-   Ideal for animating people, objects, or scenes with reliable fidelity.

##### 🎬 Cinematic Video Generation

-   Produces 5-second motion clips at **1080p or 720p** resolution.
-   Adds camera dynamics such as panning, zooming, or subtle perspective drift.
-   Supports synchronized **audio generation**, matching dialogue or ambient context.

##### 💡 Smart Prompt Adherence

-   Interprets both **text instructions** and **visual cues** for precise motion storytelling.
-   Automatically harmonizes character interactions, props, and backgrounds.

#### ⚙️ Capabilities

-   **Input:**
    
    -   Up to 3 reference images (**JPEG / PNG / WEBP**)
    -   Text prompt describing motion, action, and scene context
-   **Output:**
    
    -   8-second MP4 video (**720p or 1080p**)
    -   Optional synchronized audio
-   **Negative Prompt (optional):**
    
    -   Exclude unwanted artifacts or elements (e.g., “no text”, “no flicker”).
-   **Seed (optional):**
    
    -   Reproduce specific results for consistent creative control.

#### 💰 Pricing

| Duration | Resolution | With Audio | Without Audio |
| --- | --- | --- | --- |
| 8 seconds | 720p | **$3.20** | **$1.60** |
| 8 seconds | 1080p | **$3.20** | **$1.60** |

✅ Commercial use allowed

#### 🧩 How to Use

_(Description truncated. Full text on the model page.)_

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Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/google/veo3.1/reference-to-video · Docs: https://www.atlascloud.ai/docs