Face Swap Demo

[Myra Krallman]

Aboveis a demonstration of Phaze-A trained at 256px on consumer hardware (1x 2080Ti). The source is We Bought a Zoo, replacing Scarlett Johansson with Emma Stone. No VFX have been applied to this swap. This is raw Faceswap output. The only post-processing applied was to place in side-by-side shots of swap vs original.

This video does not demonstrate the best of what this model is capable of. It is a demonstration of some quickly chosen settings on 2 face-sets I happened to have kicking around (I'm not planning on going viral here!). The best settings for this model have definitely not been discovered yet, so there are definitely improvements to be had for this GPU.

The actual convert is done at 720p. 256px is probably the absolute minimum to get away with close-ups at this resolution, and it would probably benefit from being trained at a slightly higher resolution.

We always thought that input images needed to be the same as what the model was using - we've been using 256px face images for as long as we can remember but noticed we had better results with the latest training sets (under StoJo) when using 512px. Would 1024 fair any better?

When using legacy faces, the face is extracted from the full image at that percentage then scaled to for the model. In the case of a 256x face at 100% coverage, then the while 256x256 image would be scaled and used to train. If you're using a 256x256 model with less than 100% coverage you may need higher resolution extracts to get the full resolution.

Try not to think of the extracted faces as the training images, rather, they contain the training images. Whether you use Face or legacy centering, the actual images fed to the model will be a sub-crop from the extracted faces. Therefore, the extracted faces should always be of a higher resolution than the model input.

[mention]torzdf[/mention] - just an open request with no expectations but would it be possible for you to send over (or make available in the repo for the next update) a version of StoJo that can train at 512px (correctly, "...there's more to it"?) It'd be really interesting to do such and play with what's possible.

Hi [mention]torzdf[/mention]

Can you give us some hints about that dataset used for this video?

How many images per identity, how many different sources, did you use photos or only video frames etc.

Thanks.

Could you show your convert settings for this as well? I know convert is the most simple part of the 3 main stages, but it's the one I have the most difficulty with. I'd love to see it if you still have it.

The face swap appears smooth due to the FaceSmooth_OpacityTex texture. This is assigned to the Opacity component in the Inspector panel, which is enabled by default. Disabling the Opacity component causes the face swap to appear unnatural and rigid.

Deciding the behavior for a web font as it is loading can be animportant performance tuning technique. The new font-display descriptor for@font-face lets developers decide how their web fonts will render (or fallback),depending on how long it takes for them to load.

To mitigate some of the risk of a slow font download, most browsers implement atimeout after which a fallback font will be used. This is a useful technique butunfortunately browsers differ on the actual implementation.

To make matters worse, developers have limited control in deciding how theserules will affect their application. A performance minded developer may preferto have a faster initial experience that uses a fallback font, and only leveragethe nicer web font on subsequent visits after it has had a chance to download.Using tools like the Font Loading API, it may be possible to override some ofthe default browser behaviors and achieve performance gains, but it comes at thecost of needing to write non-trivial amounts of JavaScript which must then beinlined into the page or requested from an external file, incurring additionalHTTP latency.

To help remedy this situation the CSS Working Group has proposed a new@font-face descriptor, font-display, and a corresponding property forcontrolling how a downloadable font renders before it is fully loaded.

block gives the font face a short block period (3s is recommended in most cases)and an infinite swap period. In other words, the browser draws "invisible" textat first if the font is not loaded, but swaps the font face in as soon as itloads. To do this the browser creates an anonymous font face with metricssimilar to the selected font but with all glyphs containing no "ink."This value should only be used if rendering text in a particular typefaceis required for the page to be useable.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

iProov's analysts are tracking over 100 face swap apps and repositories, meaning that there is a wide selection of low-cost, easily accessible generative AI tools that can create highly convincing deepfakes to trick humans and some remote identity verification solutions that do a "liveness" test.

A "liveness" test will typically ask an individual to look into a webcam, perhaps turning their head from side to side, in order to prove that they are both a real person and to compare their appearance to identity documents.

The face-swapping software can create a highly convincing synthetic video, which is fed to a virtual camera that mimics a genuine webcam. This tricks a remote identity verification system into believing the subject's "liveness" and trusting their identity.

As deepfake technology is adopted more and more by identity fraudsters, an "arms race" will develop. Security firms will be battling to detect synthetic media, and the bad guys will be attempting to avoid detection.

Understand the principles of Zero Trust in cybersecurity with Tripwire's detailed guide. Ideal for both newcomers and seasoned professionals, this resource provides a practical pathway to implementing Zero Trust, enhancing your organization's security posture in the ever-evolving digital landscape.

The head swapping task aims at flawlessly placing a source head onto a target body, which is of great importance to various entertainment scenarios.While face swapping has drawn much attention, the task of head swapping has rarely been explored, particularly under the few-shot setting. It is inherently challenging due to its unique needs in head modeling and background blending.In this paper, we present the Head Swapper (HeSer), which achieves few-shot head swapping in the wild through two delicately designed modules.Firstly, a Head2Head Aligner is devised to holistically migrate pose and expression information from the target to the source head by examining multi-scale information.Secondly, to tackle the challenges of skin color variations and head-background mismatches in the swapping procedure, a Head2Scene Blender is introduced to simultaneously modify facial skin color and fill mismatched gaps on the background around the head. Particularly, seamless blending is achieved with the help of a Semantic-Guided Color Reference Creation procedure and a Blending UNet.Extensive experiments demonstrate that the proposed method produces superior head swapping results on a variety of scenes.

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Face matching is surprisingly inconsistent, and there are a few different techniques that can be used. When I created my music promotion poses, I had to use some special techniques to get the graphics to work.

In my recent article about using AI to create an album, I glossed over the techniques I used, giving a thousand-foot view of what they were. But in this article, I'm going to dive in and show you how to make it all happen, step by step.

This technique uses an existing photograph as part of the image prompt, which gives Midjourney some guidance on what to create. Since I wanted my main album promotion images to look like me, I gave Midjourney a starter image.

This link is what you'll use in preparing your prompt. Next, give Midjourney the /IMAGINE prompt, followed by the URL, and then your spec. This screenshot of the set of four generated images shows what it looked like after I entered the full prompt. The top left picture is the image I wound up using for my profile.

That said, you can see how the generated version (especially the one on the upper left) looks reasonably close to what I look like. More interestingly, the leather jacket in the image looks weirdly like the one I've been wearing for the past decade or so. How does it know?

Each quad of images generated by Midjourney has its own ID, called a seed. If you want to create future images that are similar to an existing Midjourney image, you can try referencing the seed ID. It doesn't always work, but it's worth a try.

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