Rife Video Interpolation
Playground
Try it on WavespeedAI!RIFE Video Interpolation generates smooth intermediate frames between existing video frames for higher frame rates and smoother motion. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.
Features
RIFE Video Interpolation
RIFE Video Interpolation uses the state-of-the-art Real-Time Intermediate Flow Estimation algorithm to increase your video’s frame rate by inserting synthetic frames between existing ones. The result is dramatically smoother motion — ideal for slow-motion effects, fixing choppy footage, or simply upgrading the visual quality of any clip.
Why Choose This?
-
Smooth, natural motion Synthesized frames blend seamlessly with the original footage, eliminating stutters and judder without ghosting or blurring.
-
Flexible interpolation steps Choose 1 to 4 interpolation frames per original frame pair to dial in exactly the level of smoothness you need.
-
Works on any footage Talking heads, action shots, cinematic scenes, screen recordings — RIFE adapts to a wide range of video content.
-
No local setup Upload, run, and download. No GPU required on your end.
Parameters
| Parameter | Required | Description |
|---|---|---|
| video | Yes | Input video to interpolate (URL or file upload). |
| num_frames | No | Number of frames to insert between each original frame pair. Options: 1, 2, 3, 4. |
How to Use
- Upload your video — provide the clip you want to smooth via URL or drag-and-drop.
- Set num_frames — choose how many synthetic frames to insert between each original frame pair (1 = mild, 4 = maximum smoothness).
- Submit — process, preview, and download your interpolated video.
Pricing
$0.01 per second of input video, with a minimum charge of 1 second.
Best Use Cases
- Slow-motion effects — Dramatically slow down footage while maintaining smooth, fluid motion.
- Frame rate upscaling — Convert 24fps footage to a higher effective frame rate for a more cinematic or broadcast-ready feel.
- Fixing choppy video — Smooth out low-frame-rate recordings from older cameras, screen captures, or compressed sources.
- Animation smoothing — Increase the fluidity of animated sequences without re-rendering the source.
Pro Tips
- Higher num_frames values produce smoother results but may introduce subtle artifacts on very fast motion — test with 2 first if your footage has rapid movement.
- RIFE works best on well-lit, stable footage; heavily compressed or noisy input may produce less clean interpolation.
- For slow-motion use, shoot at the highest frame rate your camera supports, then interpolate further with RIFE.
Notes
- video is the only required field.
- Pricing is based on the duration of the input video at $0.01 per second, with a minimum charge of 1 second.
- Ensure video URLs are publicly accessible if using a link rather than a direct upload.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/rife/video-interpolation" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"num_frames": 1
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| video | string | Yes | - | The URL of the video to interpolate. | |
| num_frames | integer | No | 1 | 1, 2, 3, 4 | Number of intermediate frames to generate between each pair of original frames. |
Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Array of URLs to the generated content (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction, the ID of the prediction to get |
| data.model | string | Model ID used for the prediction |
| data.outputs | string | Array of URLs to the generated content (empty when status is not completed). |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |