H3 Max Camera Controls

Turn a single image into a video with keyframed camera orbit, elevation and distance using MiniMax H3 Max via fal. The scene stays frozen; only the camera moves.

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-camera-controls
# run
$belt app run falai/minimax-h3-max-camera-controls --input '{"image":"https://example.com/file.png","camera_trajectory":[{"azimuth":0,"distance":1,"elevation":10,"time":0},{"azimuth":90,"distance":1,"elevation":10,"time":1}]}'
maker
falai
category
video
input → output
image → video
price
$0.05/sec 480P, $0.08/sec 768P, $0.16/sec 1080P
runs via
api, javascript and python sdk, belt cli, mcp
updated
2026-10-20

api reference

about

turn a single image into a video with keyframed camera orbit, elevation and distance using minimax h3 max via fal. the scene stays frozen; only the camera moves.

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-camera-controls",9        "input": {10            "image": "https://example.com/file.png",11            "camera_trajectory": [12                {13                    "azimuth": 0,14                    "distance": 1,15                    "elevation": 10,16                    "time": 017                },18                {19                    "azimuth": 90,20                    "distance": 1,21                    "elevation": 10,22                    "time": 123                }24            ]25        }26    })2728print(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-camera-controls",10        "input": {11            "image": "https://example.com/file.png",12            "camera_trajectory": [13                {14                    "azimuth": 0,15                    "distance": 1,16                    "elevation": 10,17                    "time": 018                },19                {20                    "azimuth": 90,21                    "distance": 1,22                    "elevation": 10,23                    "time": 124                }25            ]26        }27    }, stream=True):28    if update.get("progress"):29        print(f"progress: {update['progress']}%")30    if update.get("output"):31        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-camera-controls",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-camera-controls",3    "input": {4        "image": "https://example.com/file.png",5        "camera_trajectory": [6            {7                "azimuth": 0,8                "distance": 1,9                "elevation": 10,10                "time": 011            },12            {13                "azimuth": 90,14                "distance": 1,15                "elevation": 10,16                "time": 117            }18        ]19    },20    "webhook": "https://your-server.com/webhook"21}, 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

imagestring(file)*

image to use as the first frame.

camera_trajectoryarray*

ordered camera keyframes (max 12). the first pose is held before its time and the last pose for the remainder.

example: [{"azimuth":0,"distance":1,"elevation":10,"time":0},{"azimuth":90,"distance":1,"elevation":10,"time":1}]
promptstring

optional prompt. defaults to preserving the frozen scene while only the camera moves.

durationnumber

video duration in seconds (1-15).

default: 5min:1max:15
resolutionstring

480p native (default), 768p native, 1080p latent refinement.

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

prompt rewriting effort.

default: "balanced"
options:"disabled""balanced""quality"
seedinteger

random seed for reproducible generation.

output

expanded_promptstring

the prompt after expansion.

videostring(file)*

the generated video.

ready to run H3 Max Camera Controls?

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