DEV Community: mix

How to Try Live Face Swap Online for free?

mix — Sat, 01 Aug 2026 15:26:19 +0000

You no longer need to install a local AI model just to test real-time face swap. With LiveFaceSwap AI, you can choose a portrait, open your webcam, and compare the camera input with the AI output directly in your browser.

Here is the shortest useful workflow for running your first test.

What you need

Prepare a webcam, a stable internet connection, and a portrait you own or have permission to use.

A clear, front-facing photo usually works better than a group photo or an image with heavy blur, strong shadows, sunglasses, or a partially hidden face.

1. Open the workspace

Go to the LiveFaceSwap AI browser workspace.

Face Swap is selected by default. Choose one of the reference presets, or click Upload to provide your own image.

For the first test, keep the same portrait, model, lighting, and camera position. Changing several things at once makes it difficult to understand what affected the result.

2. Turn on your camera

Click Open camera, then allow camera access when your browser asks for permission.

Check that the correct camera appears and that your face is clearly visible. If the camera does not open, review the site's camera permission and close any meeting or recording application that may already be using the device.

Opening the camera only starts a local preview. It does not start cloud AI processing or consume live-session credits.

3. Start Face Swap

Check the selected model, displayed credit rate, and available balance. The rate can vary by model, so use the current value shown in the workspace.

Click Start Face Swap. If the sign-in dialog opens, sign in and click Start Face Swap again. On your first session, review and accept the safety confirmation; the workspace will then start preparing the AI output.

Once the first AI output frame appears, compare it with the original camera input.

A live face-swap example from the AutoFill media set. Actual output varies with the reference, camera, movement, network, and selected model.

Try a neutral expression, a few small head turns, and ordinary facial movements. Keep the test short and change only one variable before trying again—for example, the portrait, lighting, framing, or model.

Because the AI processing runs in the cloud, the result can be affected by the reference image, camera quality, movement, network conditions, and selected model. Real time does not mean zero delay or identical output in every frame.

4. Stop and review

Click Stop preview when you have seen enough. Wait for the session to stop and for the final credit balance to update before starting another test.

If you can see the camera but no AI output, check the workspace status, reference portrait, connection, account state, and available credits. A visible webcam preview confirms camera access, but it does not necessarily mean the cloud AI session has started.

The browser workspace is intended for live preview and comparison. It does not currently record, download, export, or save the transformed video.

Use face swap responsibly

Only use your own likeness and camera feed, or material you have explicit permission to use. Do not use face swap for impersonation, fraud, harassment, or presenting synthetic footage as authentic evidence. If a realistic result could mislead viewers, disclose that it is AI-generated.

That is the complete browser workflow: choose an authorized portrait, check the camera, confirm the displayed rate, start the AI session, and adjust one variable at a time.

How to Do a Real-Time Video Face Swap in Your Browser

mix — Thu, 23 Jul 2026 15:48:01 +0000

To swap faces in real time, you need a reference portrait, a webcam, and a live AI preview. The portrait supplies the new face; your webcam supplies the movement and expressions. Here is the shortest workflow using Vivify Realtime Face Swap.

The shortest browser workflow

Face Swap mode and a sample portrait are already selected.

Keep the sample or upload an authorized face, check the displayed rate, then click Start live preview.
Allow camera access when the browser asks.
Compare Camera with AI output and make a few slow head turns.

What “real time” means here

An offline face swap returns a finished file. A live swap keeps processing new webcam frames with the reference face. One good frame is not enough: turn your head or change your expression, and confirm that the AI output continues updating.

If it does not work

No camera image: check the site's camera permission and close another app if it is using the webcam.
Camera works, but AI output does not: retry the live session; this is an output or connection problem, not a camera-permission problem.
The first frame looks good, but motion breaks: use slower movement, a clear reference portrait, and even lighting.

Use it responsibly

Use your own face or an explicitly authorized reference. Do not use live face swap for impersonation, fraud, or bypassing identity checks, and disclose realistic synthetic media when the destination platform requires it.

The browser page is preview-only: it does not record, download, or save the transformed stream. To use the result in OBS, Zoom, or another app, you need a separate virtual-camera client.

We Built a Live Face Swap Preview in the Browser

mix — Sun, 12 Jul 2026 14:17:58 +0000

We built Vivify's Live Face Swap to transform a webcam feed while the session is running.

Unlike an upload-and-wait face swap, it responds to movement and expression in the browser. The most interesting work was keeping access short-lived, metering an open-ended session, and deciding what the first release should not do.

What you can try in the browser

Upload a reference image and press Start. The transformed stream appears beside the camera feed, so movement and expression are visible without rendering a clip first.

Two design challenges

Session access: the server issues a short-lived credential when the preview starts, keeping the reusable provider key off the client.
Open-ended usage: unlike a normal generation job, a live session has no fixed duration. We meter active time with heartbeats, settle the final partial segment when it ends, and show usage while it runs.

Where the first release stops

The browser version is preview-only: it does not record, export, or save the transformed stream. We also ask users to use only their own face or images they have permission to use.

Try live face swap browser preview on Vivify. What would you test first: identity consistency, latency, expression preservation, or recovery after occlusion?

I Tried Adding Image OCR to a Morse Code Translator

mix — Wed, 13 May 2026 07:03:58 +0000

I thought adding image support to a morse code translator would be straightforward.

Text Morse is deterministic enough: split the dots and dashes into groups, map the groups to letters, then handle spaces between words. An image felt like the same pipeline with OCR in front of it.

That assumption broke as soon as I looked at real inputs.

Screenshots get compressed. Photos are tilted. Scans have uneven lighting. Dots and dashes may be visible to a human, but the spacing between them is often too fuzzy for a tool to trust.

The first attempt: clean the image

The obvious first step was preprocessing.

I tried the usual OCR cleanup ideas:

convert the image to black and white
increase contrast
crop around the Morse area
reduce noise
handle slight rotation or skew
make the marks easier for OCR to see

These steps help in some cases. They are also dangerous.

Morse code is not just “visible marks.” The gaps are part of the data. If preprocessing makes a dot too thick, removes a faint dash, or merges two spaces, the final decoded text can change.

That was the first useful lesson: a cleaner-looking image is not always a more faithful Morse image.

Why general OCR is a poor fit

Most OCR tools are designed around letters, words, and lines of text.

Morse code is closer to a timing or segmentation problem. The tool needs to preserve:

short marks
long marks
gaps inside a character
gaps between characters
larger gaps between words

A human can look at a messy image and infer intent. A tool has to decide from pixels.

If a photo is taken at an angle, the same visual distance may mean different things across the line. If the image is blurry, a dot may look like noise. If the spacing is inconsistent, the OCR result can look plausible while still being wrong.

Browser OCR vs backend recognition

I also tried thinking about where recognition should happen.

Browser-side OCR is attractive because it feels immediate. The user uploads an image and gets a result without waiting for a backend job. It also keeps the workflow simple.

Backend OCR or model-based recognition gives more flexibility. You can try heavier models, orientation detection, or multiple passes. But it adds cost, latency, deployment complexity, and another place where the result can become a black box.

I looked at newer OCR model families too. PaddleOCR and Transformer-based OCR approaches like TrOCR are useful references, especially for messy inputs. But Morse images are not ordinary text images. The model still needs to preserve dot-dash structure and spacing in a form that a Morse decoder can trust.

The current product decision

In Morse Coder, I am treating image decoding as an assisted workflow instead of a magic button.

The current direction is:

Let the user upload an image.
Try to extract the Morse pattern automatically.
Use AI-assisted recognition when normal OCR struggles.
Keep manual Morse input available.
Let the user inspect or correct the extracted Morse before trusting the decoded text.

That manual correction step is not just a fallback. It is part of the design.

If the tool jumps straight from image to final text, the user has no way to know whether an error came from image cleanup, mark detection, spacing, or Morse translation. Showing the intermediate Morse layer gives the user something concrete to repair.

You can try the current image workflow in Morse Coder.

The open question

The part I am still unsure about is how much the tool should guess.

For puzzle users, a good guess may be enough. For historical material, learning material, or classroom use, inspecting the Morse layer may matter more.

Right now I am leaning toward a hybrid: automate what is safe, expose the intermediate result, and make correction easy.

If you have any suggestions for this problem, I’d love to hear them.