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HomeSoftware DevelopmentUndertaking GameFace makes gaming accessible to everybody — Google for Builders Weblog

Undertaking GameFace makes gaming accessible to everybody — Google for Builders Weblog

Posted by Avneet Singh, Product Supervisor and Sisi Jin, UX Designer, Google PI, and Lance Carr, Collaborator

At I/O 2023, Google launched Undertaking Gameface, an open-source, hands-free gaming ‘mouse’ enabling individuals to manage a pc’s cursor utilizing their head motion and facial gestures. Individuals can increase their eyebrows to click on and drag, or open their mouth to maneuver the cursor, making gaming extra accessible.

The venture was impressed by the story of quadriplegic online game streamer Lance Carr, who lives with muscular dystrophy, a progressive illness that weakens muscle tissues. And we collaborated with Lance to deliver Undertaking Gameface to life. The total story behind the product is offered on the Google Key phrase weblog right here.

It’s been a particularly fascinating expertise to consider how a mouse cursor might be managed in such a novel method. We carried out many experiments and located head motion and facial expressions could be a distinctive option to program the mouse cursor. MediaPipe’s new Face Landmarks Detection API with blendshape possibility made this attainable because it permits any developer to leverage 478 three-dimensional face landmarks and 52 blendshape scores (coefficients representing facial features) to deduce detailed facial surfaces in real-time.

Product Assemble and Particulars

On this article, we share technical particulars of how we constructed Undertaking Gamefaceand the assorted open supply applied sciences we leveraged to create the thrilling product!

Utilizing head motion to maneuver the mouse cursor

Moving image showing how the user controls cursor speed
Caption: Controlling head motion to maneuver mouse cursors and customizing cursor velocity to adapt to completely different display resolutions.

By this venture, we explored the idea of utilizing the pinnacle motion to have the ability to transfer the mouse cursor. We targeted on the brow and iris as our two landmark areas. Each brow and iris landmarks are recognized for his or her stability. Nonetheless, Lance observed that the cursor did not work nicely whereas utilizing the iris landmark. The rationale was that the iris could transfer barely when individuals blink, inflicting the cursor to maneuver unintendedly. Subsequently, we determined to make use of the brow landmark as a default monitoring possibility.

There are situations the place individuals could encounter challenges when transferring their head in sure instructions. For instance, Lance can transfer his head extra shortly to the appropriate than left. To deal with this subject, we launched a user-friendly resolution: separate cursor velocity adjustment for every route. This characteristic permits individuals to customise the cursor’s motion in response to their preferences, facilitating smoother and extra snug navigation.

We needed the expertise to be as clean as a handheld controller. Jitteriness of the mouse cursor is among the main issues we needed to beat. The looks of cursor jittering is influenced by numerous elements, together with the person setup, digital camera, noise, and lighting situations. We applied an adjustable cursor smoothing characteristic to permit customers the comfort of simply fine-tuning this characteristic to greatest go well with their particular setup.

Utilizing facial expressions to carry out mouse actions and keyboard press

Very early on, one in all our main insights was that individuals have various consolation ranges making completely different facial expressions. A gesture that comes simply to at least one person could also be extraordinarily tough for an additional to do intentionally. As an example, Lance can transfer his eyebrows independently with ease whereas the remainder of the group struggled to match Lance’s talent. Therefore, we determined to create a performance for individuals to customise which expressions they used to manage the mouse.

Moving image showing how the user controls the cursor using their facial expressions
Caption: Utilizing facial expressions to manage mouse

Consider it as a customized binding of a gesture to a mouse motion. When deliberating about which mouse actions ought to the product cowl, we tried to seize widespread situations resembling left and proper click on to scrolling up and down. Nonetheless, utilizing the pinnacle to manage mouse cursor motion is a special expertise than the standard method. We needed to present the customers the choice to reset the mouse cursor to the middle of the display utilizing a facial gesture too.

Moving image showing how the user controls the keyboard using their facial expressions
Caption: Utilizing facial expressions to manage keyboard

The latest launch of MediaPipe Face Landmarks Detection brings an thrilling addition: blendshapes output. With this enhancement, the API generates 52 face blendshape values which symbolize the expressiveness of 52 facial gestures resembling elevating left eyebrow or mouth opening. These values might be successfully mapped to manage a variety of capabilities, providing customers expanded prospects for personalization and manipulation.

We’ve been in a position to lengthen the identical performance and add the choice for keyboard binding too. This helps use their facial gestures to additionally press some keyboard keys in an analogous binding vogue.

Set Gesture Measurement to see when to set off a mouse/keyboard motion

Moving image showing setting the gesture size to trigger an action
Caption: Set the gesture dimension to set off an motion

Whereas testing the software program, we discovered that facial expressions have been roughly pronounced by every of us, so we’ve included the concept of a gesture dimension, which permits individuals to manage the extent to which they should gesture to set off a mouse motion. Blendshapes coefficients have been useful right here and completely different customers can now set completely different thresholds on every particular expression and this helps them customise the expertise to their consolation.

Maintaining the digital camera feed out there

One other key perception we obtained from Lance was players usually have a number of cameras. For our machine studying fashions to function optimally, it’s greatest to have a digital camera pointing straight to the person’s face with first rate lighting. So we’ve included the power for the person to pick the proper digital camera to assist body them and provides probably the most optimum efficiency.

Our product’s person interface incorporates a stay digital camera feed, offering customers with real-time visibility of their head actions and gestures. This characteristic brings a number of benefits. Firstly, customers can set thresholds extra successfully by straight observing their very own actions. The visible illustration allows knowledgeable selections on acceptable threshold values. Furthermore, the stay digital camera feed enhances customers’ understanding of various gestures as they visually correlate their actions with the corresponding actions within the utility. Total, the digital camera feed considerably enhances the person expertise, facilitating correct threshold settings and a deeper comprehension of gestures.

Product Packaging

Our subsequent step was to create the power to manage the mouse and keyboard utilizing our customized outlined logic. To allow mouse and keyboard management inside our Python utility, we make the most of two libraries: PyAutoGUI for mouse management and PyDirectInput for keyboard management. PyAutoGUI is chosen for its strong mouse management capabilities, permitting us to simulate mouse actions, clicks, and different actions. However, we leverage PyDirectInput for keyboard management because it gives enhanced compatibility with numerous purposes, together with video games and people counting on DirectX.

For our utility packaging, we used PyInstaller to show our Python-based utility into an executable, making it simpler for customers to run our software program with out the necessity for putting in Python or further dependencies. PyInstaller gives a dependable and environment friendly means to distribute our utility, guaranteeing a clean person expertise.

The product introduces a novel kind issue to interact customers in an vital perform like dealing with the mouse cursor. Making the product and its UI intuitive and simple to comply with was a high precedence for our design and engineering group. We labored carefully with Lance to include his suggestions into our UX issues, and we discovered CustomtKinter was in a position to deal with most of our UI issues in Python.

We’re excited to see the potential of Undertaking GameFace and may’t anticipate builders and enterprises to leverage it to construct new experiences. The code for GameFace is open sourced on Github right here.


We want to acknowledge the invaluable contributions of the next individuals to this venture: Lance Carr, David Hewlett, Laurence Moroney, Khanh LeViet, Glenn Cameron, Edwina Priest, Joe Fry, Feihong Chen, Boon Panichprecha, Dome Seelapun, Kim Nomrak, Pear Jaionnom, Lloyd Hightower



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