minimax/h3/reference-to-video

image-to-video

MiniMax H3 Reference-to-Video API by MiniMax

minimax/h3/reference-to-video

Reference-to-video

MiniMax H3 reference-to-video: generate a video that keeps the subject from a reference image, driven by a text prompt. Supports 2K, 5-15s.

MiniMax H3 Reference-to-Video is developed by MiniMax. Atlas Cloud (operated by Atlas Cloud AI LLC) provides access to it and does not own it. All trademarks belong to their respective owners.

INPUT

Prompt *

Reference Materials *(0/9)

You can drag and drop a file or click to upload

MAX:9

Resolution *

Duration *

Aspect Ratio

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

Your request will cost $0.1 per run. For $10 you can run this model approximately 100 times.

Here's what you can do next:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parameters

Code Example
import requests
import time

# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "minimax/h3/reference-to-video",  # Required. model name
    "prompt": "A beautiful sunset over the ocean with gentle waves",  # Required. The text prompt describing the video to generate
    "refers": [
        {
            "url": "example_url",
            "type": "example_type"
        }
    ],  # Required. Reference materials that anchor the generation
    "resolution": "2K",  # Required. The resolution of the generated video. options: 768P | 2K
    "duration": 8,  # Required. The duration of the generated video in seconds
    "ratio": "adaptive",  # The aspect ratio of the generated video
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] in ["completed", "succeeded"]:
            print("Generated video:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

video_url = check_status()

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

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

HTTP Headers

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateVideo

Request Body

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "minimax/h3/reference-to-video",
    "prompt": "A beautiful sunset over the ocean with gentle waves"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

Response

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Check Status

Poll the prediction endpoint to check the current status of your request.

GET/api/v1/model/prediction/{prediction_id}

Polling Example

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp4"
    ],
    "metrics": {
      "predict_time": 45.2
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Response

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

The following parameters are accepted in the request body.

Total: 6Required: 5Optional: 1

modelstringrequired

model name

Default: "minimax/h3/reference-to-video"

promptstringrequired

The text prompt describing the video to generate.

refersarray[object]required

Reference materials that anchor the generation. Any mix of reference images, videos, and audio, each as a public URL. At least one image OR video is required (audio alone is not allowed). Image formats: png, jpeg, jpg, webp; video: mp4, mov; audio: mp3, wav. 'type' is optional — when omitted it is inferred from the URL extension.

Min items: 1

urlstringrequired

Public URL of the reference image / video / audio.

typestring

Media kind. Optional; inferred from the URL when omitted.

imagevideoaudio

resolutionstringrequired

The resolution of the generated video.

Default: "2K"

768P2K

durationnumberrequired

The duration of the generated video in seconds.

Default: 8

456789101112131415

ratiostring

The aspect ratio of the generated video. Use 'adaptive' to let the model choose.

Default: "adaptive"

adaptive21:916:94:31:13:49:16

Example Request Body

{
  "model": "minimax/h3/reference-to-video",
  "prompt": "A beautiful landscape",
  "refers": [
    {
      "url": "example_url",
      "type": "example_type"
    }
  ],
  "resolution": "2K",
  "duration": 8,
  "ratio": "adaptive"
}

Output Schema

The API returns a prediction response with the generated output URLs.

created_atstring

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

idstring

Unique identifier for the prediction, the ID of the prediction to get.

modelstring

Model ID used for the prediction.

outputsarray

Array of URLs to the generated content (empty when status is not completed).

statusstring

Status of the task: created, processing, completed, or failed.

Example Response

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.mp4"
  ],
  "metrics": {
    "predict_time": 45.2
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrates 400+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

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

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 400+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 400+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "minimax/h3/reference-to-video"
          },
          "prompt": {
            "description": "The text prompt describing the video to generate.",
            "type": "string"
          },
          "refers": {
            "description": "Reference materials that anchor the generation. Any mix of reference images, videos, and audio, each as a public URL. At least one image OR video is required (audio alone is not allowed). Image formats: png, jpeg, jpg, webp; video: mp4, mov; audio: mp3, wav. 'type' is optional — when omitted it is inferred from the URL extension.",
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "url": {
                  "type": "string",
                  "description": "Public URL of the reference image / video / audio.",
                  "x-ui-component": "uploaders"
                },
                "type": {
                  "type": "string",
                  "description": "Media kind. Optional; inferred from the URL when omitted.",
                  "enum": [
                    "image",
                    "video",
                    "audio"
                  ]
                }
              },
              "required": [
                "url"
              ]
            },
            "minItems": 1,
            "x-ui-component": "uploaders"
          },
          "resolution": {
            "default": "2K",
            "description": "The resolution of the generated video.",
            "enum": [
              "768P",
              "2K"
            ],
            "type": "string",
            "x-ui-component": "select"
          },
          "duration": {
            "default": 8,
            "description": "The duration of the generated video in seconds.",
            "enum": [
              4,
              5,
              6,
              7,
              8,
              9,
              10,
              11,
              12,
              13,
              14,
              15
            ],
            "type": "number",
            "x-ui-component": "select"
          },
          "ratio": {
            "default": "adaptive",
            "description": "The aspect ratio of the generated video. Use 'adaptive' to let the model choose.",
            "enum": [
              "adaptive",
              "21:9",
              "16:9",
              "4:3",
              "1:1",
              "3:4",
              "9:16"
            ],
            "type": "string",
            "x-ui-component": "select"
          }
        },
        "required": [
          "model",
          "prompt",
          "resolution",
          "duration",
          "refers"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "refers",
          "resolution",
          "duration",
          "ratio"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "created_at": {
            "description": "ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).",
            "format": "date-time",
            "type": "string"
          },
          "has_nsfw_contents": {
            "description": "Array of boolean values indicating NSFW detection for each output.",
            "items": {
              "type": "boolean"
            },
            "type": "array"
          },
          "id": {
            "description": "Unique identifier for the prediction, the ID of the prediction to get.",
            "type": "string"
          },
          "model": {
            "description": "Model ID used for the prediction.",
            "type": "string"
          },
          "outputs": {
            "description": "Array of URLs to the generated content (empty when status is not completed).",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "status": {
            "description": "Status of the task: created, processing, completed, or failed.",
            "type": "string"
          },
          "urls": {
            "description": "Object containing related API endpoints.",
            "type": "object"
          }
        },
        "type": "object"
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  },
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "openapi": "3.0.0",
  "paths": {
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "description": "Request ID",
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        }
      },
      "x-api-name": "model_result"
    },
    "/api/v1/model/generateVideo": {
      "post": {
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        }
      },
      "x-api-name": "model_run"
    }
  },
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ]
}

LLM-Friendly Prompt Template

# minimax/h3/reference-to-video

> MiniMax H3 reference-to-video: generate a video that keeps the subject from a reference image, driven by a text prompt. Supports 2K, 5-15s.


## Overview

- **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**: `minimax/h3/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: `"minimax/h3/reference-to-video"`

- **`prompt`** (`string`, _required_):
  The text prompt describing the video to generate.

- **`refers`** (`array[object]`, _required_):
  Reference materials that anchor the generation. Any mix of reference images, videos, and audio, each as a public URL. At least one image OR video is required (audio alone is not allowed). Image formats: png, jpeg, jpg, webp; video: mp4, mov; audio: mp3, wav. 'type' is optional — when omitted it is inferred from the URL extension.
  - Min items: 1
  - Item properties:
    - **`url`** (`string`, _required_):
      Public URL of the reference image / video / audio.

    - **`type`** (`string`, _optional_):
      Media kind. Optional; inferred from the URL when omitted.
      - Options: "image", "video", "audio"


- **`resolution`** (`string`, _required_):
  The resolution of the generated video.
  - Default: `"2K"`
  - Options: "768P", "2K"

- **`duration`** (`number`, _required_):
  The duration of the generated video in seconds.
  - Default: `8`
  - Options: 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15

- **`ratio`** (`string`, _optional_):
  The aspect ratio of the generated video. Use 'adaptive' to let the model choose.
  - Default: `"adaptive"`
  - Options: "adaptive", "21:9", "16:9", "4:3", "1:1", "3:4", "9:16"



**Required Parameters Example**:

```json
{
  "model": "minimax/h3/reference-to-video",
  "prompt": "",
  "resolution": "2K",
  "duration": 8,
  "refers": [
    {
      "url": "",
      "type": "image"
    }
  ]
}
```


**Full Example**:

```json
{
  "model": "minimax/h3/reference-to-video",
  "prompt": "",
  "refers": [
    {
      "url": "",
      "type": "image"
    }
  ],
  "resolution": "2K",
  "duration": 8,
  "ratio": "adaptive"
}
```


### 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": "minimax/h3/reference-to-video",
  "prompt": "",
  "refers": [
    {
      "url": "",
      "type": "image"
    }
  ],
  "resolution": "2K",
  "duration": 8,
  "ratio": "adaptive"
}'

# 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/minimax/h3/reference-to-video)

A little bird is hopping happily.

MiniMax H3 Reference-to-Video

MiniMax H3 Reference-to-Video generates a video that keeps the subjects from your reference materials consistent throughout, driven by your text prompt. Instead of animating a fixed frame, it uses the references as identity anchors — ideal for placing a character, product, or style into new scenes and motions. References can be any mix of images, videos, and an audio track (e.g. a music beat) to sync the motion to.

Why Choose This?

Subject consistency Preserve one or several people, characters, or objects' identities across the whole clip.
Mix reference types Combine reference images, reference videos, and reference audio in a single request.
Audio sync (optional) Provide a reference audio track and sync the action to the beat.
Prompt-driven scenes Put the reference subjects into any scene or action you describe.
High resolution output Generate videos in 2K quality.
Flexible aspect ratios adaptive, 21:9, 16:9, 4:3, 1:1, 3:4, or 9:16.
Selectable duration Produce 4–15s clips.

Parameters

Parameter	Required	Description
prompt	Yes	Text description of the scene and action for the subjects
refers	Yes	Array of reference materials, each `{ "url": "...", "type": "image\|video\|audio" }`. `type` is optional (inferred from the URL). At least one image OR video is required; audio alone is not allowed.
resolution	Yes	Video resolution. Available options: `768P`, `2K`
duration	Yes	Duration of the generated video in seconds. Integer between 4 and 15. Available options: 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
ratio	No	Aspect ratio of the generated video. Defaults to `adaptive` (the most suitable ratio is chosen automatically based on the input). You may also explicitly specify a concrete ratio. Available options: `adaptive`, `21:9`, `16:9`, `4:3`, `1:1`, `3:4`, `9:16`

`refers` example

"refers": [
  { "url": "https://.../subject1.png", "type": "image" },
  { "url": "https://.../subject2.png" },
  { "url": "https://.../clip.mp4", "type": "video" },
  { "url": "https://.../beat.mp3", "type": "audio" }
]

How to Use

Add your references — any mix of images and/or videos (at least one), optionally plus an audio track.
Write your prompt — describe the scene, action, and camera movement for the subjects.
Set resolution and duration — balance quality against generation speed.
Choose an aspect ratio — match your target platform, or use adaptive.
Run — submit and download your video.

Pricing

Output — billed per second of generated video, by resolution:

Resolution	Cost per second
2K	$0.14
768p	$0.10

Best Use Cases

Character Consistency — Keep the same character across multiple shots.
Product Placement — Feature a specific product in generated scenes.
Branded Content — Maintain a consistent mascot or spokesperson.
Creative Series — Build multi-clip stories around a recurring subject.

Pro Tips

Use clean, well-lit reference images with each subject clearly visible.
Give one image per subject when combining several subjects in one video.
Describe the new scene and action, not the reference materials themselves.
Supply a reference audio track to drive the timing/beat of the motion.
Combine with different ratios and durations to repurpose the same subjects across platforms.

Notes

prompt and at least one reference image or video are required; a request with only audio is rejected.
Reference-to-video and image-to-video are mutually exclusive: this model uses reference materials, not first/last frames — do not mix the two.
Ensure all reference URLs (image / video / audio) are publicly accessible.
Generation is asynchronous — submit, then poll for the finished video.

MiniMax H3 Text-to-Video — Generate video directly from a text prompt.
MiniMax H3 Image-to-Video — Animate a first-frame image (optionally with a last frame).

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From

$0.1/SEC

text-to-video

Hailuo-2.3 t2v Standard

High-quality text-to-video generation optimized for creative workflows with cinematic visuals and reliable prompt fidelity.

Hailuo-2.3 t2v Pro

Professional-grade text-to-video model delivering advanced motion, physics realism and film-style output for VFX and marketing.

From

$0.49/SEC

image-to-video

Hailuo-2.3 i2v Standard

Image-to-video conversion model offering efficient animation from stills with consistent style and smooth motion.

Hailuo-2.3 i2v Pro

Premium image-to-video model designed for detailed scene evolution, character continuity and high-fidelity animation.

From

$0.49/SEC

image-to-video

Hailuo-2.3 Fast

Speed-optimized variant of Hailuo-2.3 delivering rapid video generation while maintaining strong visual quality for quick iterations.

Hailuo-02 t2v Pro

Hailuo 02 is a new AI video generation model from Hailuo AI.

Hailuo-02 Fast

Hailuo 02 is a new AI video generation model from Hailuo AI.

Hailuo 02 Pro

Hailuo 02 is a new AI video generation model from Hailuo AI.

Hailuo 02 t2v Standard

Hailuo 02 is a new AI video generation model from Hailuo AI.

Hailuo 02 i2v Standard

Hailuo 02 is a new AI video generation model from Hailuo AI.

Hailuo 02 i2v Pro

Hailuo 02 is a new AI video generation model from Hailuo AI.

Hailuo 02 Standard

Hailuo 02 Standard - MiniMax's next-generation AI video model with 2.5x efficiency improvement, 85% complex instruction response rate, and industry-leading cost-effectiveness for generating high-quality videos.

Sync.so Lipsync v3

Sync.so Lipsync v3 (sync-3) is Sync Labs state-of-the-art lip-sync model, re-syncing the lips of an existing video to a new audio track with industry-leading naturalness.

VEED Lipsync

VEED Lipsync re-drives the lip movements of an existing talking-head video to match a new audio track, preserving identity, appearance and background.

LIP-SYNC

From

$0.013/SEC

One API for All Media AI.

Explore all models

MiniMax H3 Reference-to-Video API by MiniMax

INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example

Response

Input Schema

Example Request Body

Output Schema

Example Response

Atlas Cloud Skills

Supported Clients

Install

Setup API Key

Capabilities

MCP Server

Supported Clients

Install

Configuration

Available Tools

API Schema

LLM-Friendly Prompt Template

MiniMax H3 Reference-to-Video

Why Choose This?

Parameters

refers example

How to Use

Pricing

Best Use Cases

Pro Tips

Notes

Related Models

Explore Similar Models

MiniMax H3 Text-to-Video

MiniMax H3 Image-to-Video

Hailuo-2.3 t2v Standard

Hailuo-2.3 t2v Pro

Hailuo-2.3 i2v Standard

Hailuo-2.3 i2v Pro

Hailuo-2.3 Fast

Hailuo-02 t2v Pro

Hailuo-02 Fast

Hailuo 02 Pro

Hailuo 02 t2v Standard

Hailuo 02 i2v Standard

Hailuo 02 i2v Pro

Hailuo 02 Standard

Sync.so Lipsync v3

VEED Lipsync

One API for All Media AI.

INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example

`refers` example