H3 Max 3D to Video

Turn a Blender or 3D previs render into photorealistic video with MiniMax H3 Max via fal. Layout, camera and timing follow the input; optional reference images set the look.

run with your agent
# install belt
$curl -fsSL https://cli.inference.sh | sh
# sign in (or set INFERENCE_API_KEY on a headless machine)
$belt login
# view schema & details
$belt app get falai/minimax-h3-max-3d-to-video
# run
$belt app run falai/minimax-h3-max-3d-to-video --input '{"video":"https://example.com/file.png"}'
maker
falai
category
video
input → output
video → video, 3d → video
price
$0.50/request + $0.05/sec (480P), $0.08/sec (768P), $0.16/sec (1080P) + $0.10/generated ref; 5s min
runs via
api, javascript and python sdk, belt cli, mcp
updated
2026-10-20

api reference

about

turn a blender or 3d previs render into photorealistic video with minimax h3 max via fal. layout, camera and timing follow the input; optional reference images set the look.

1. calling the api

install the client

the client provides a convenient way to interact with the api.

bash
1pip install inferencesh

setup your api key

set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.

bash
1export INFERENCE_API_KEY="inf_your_key"

run and get result

submit a request and wait for the final result. best for batch processing or when you don't need progress updates.

python
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567result = client.run({8        "app": "falai/minimax-h3-max-3d-to-video",9        "input": {10            "video": "https://example.com/file.png"11        }12    })1314print(result["output"])

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.

python
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567# stream=True yields updates as they arrive8for update in client.run({9        "app": "falai/minimax-h3-max-3d-to-video",10        "input": {11            "video": "https://example.com/file.png"12        }13    }, stream=True):14    if update.get("progress"):15        print(f"progress: {update['progress']}%")16    if update.get("output"):17        print(f"output: {update['output']}")

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.

python
1# local file paths are automatically uploaded2result = client.run({3    "app": "falai/minimax-h3-max-3d-to-video",4    "input": {5        "image": "/path/to/local/image.png",  # detected & uploaded6        "audio": "https://example.com/audio.mp3",  # url passed through7    }8})

manual upload

you can also upload files manually and use the returned url.

python
1# upload and get a hosted URL2file = client.files.upload("/path/to/file.png")3print(file.uri)  # https://cloud.inference.sh/...

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

python
1result = client.run({2    "app": "falai/minimax-h3-max-3d-to-video",3    "input": {4        "video": "https://example.com/file.png"5    },6    "webhook": "https://your-server.com/webhook"7}, wait=False)

webhook payload

your endpoint receives a JSON POST with the task result:

json
1{2  "event": "task.completed",3  "timestamp": "2024-01-15T10:30:05Z",4  "data": {5    "id": "task_abc123",6    "short_id": "abc123",7    "status": 10,8    "status_text": "completed",9    "output": { ... },10    "created_at": "2024-01-15T10:30:00Z",11    "updated_at": "2024-01-15T10:30:05Z"12  }13}
eventstringtask.completed, task.failed or task.cancelled
timestampstringiso timestamp of the delivery
data.idstringtask id
data.short_idstringshort task id
data.statusnumberterminal status (10=completed, 11=failed, 12=cancelled, 13=rejected)
data.status_textstringthe status as a word: completed, failed or cancelled
data.outputobjecttask output (when completed)
data.errorstringerror message (when failed, omitted otherwise)
data.session_idstringsession id (when the task ran in a session, omitted otherwise)
data.created_atstringiso timestamp
data.updated_atstringiso timestamp

5. schema

input

videostring(file)*

blender or previs render, up to 15 seconds and 32 shots.

promptstring

optional clarification of what the proxies represent or how they move, e.g. 'the moving block is a running person.'

reference_imagesarray

optional environment, subject, interior or detail references (max 8). used directly, without generating new images.

max_generated_reference_imagesinteger

max new appearance references to generate when no reference images are given. each is billed by fal.

default: 2min:1max:8
resolutionstring

output quality. duration follows the input video.

default: "768P"
options:"480P""768P""1080P"

output

videostring(file)*

the finished realistic video, matching the input duration.

ready to run H3 Max 3D to Video?

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