Castor Troy: [during the shootout at the apartment] I don't know what I hate wearing worse: your face or your body. I mean I enjoy *boning* your wife, but let's face it, we both like it better the other way, yes? So why don't we trade back.
Face Off sub download
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Spirit of a lion describes my soulGive it up to zion then my fire growsWishing of a riot inside my lobeAm I trippin'? It's the highest when I fight my foes!Bip, I'ma hit 'em with this, you 'bout to get yo' ass kickedMy technique is so sick, I'm 'bout to make the hit quickWhole lotta muscle (chea) you don't wanna be in a tussle (chea)Better than me? That's rubbleYou want trouble? What's up though? (chea)Feelings hot to kill a top gorillaDrop and kneel to pops, I grilled the oppFor real, ya clock, it yield and stopYa will is shot, the deal was knock ya till ya plopI got the hell comin' yo way (chеa)You softer than a thing of yoplaitNever compеtitive with the better kid, I go crayGonna be deaded because I fed it to the mo' graves (chea)When you wake up in that wet bloodAin't no way you 'bout to get upHow you know that I taxed yo' ass?'Cause you got the motherfuckin' check stub (rrr)Always gonna have to pay costWhen you steppin' in my way, bossWhen I build steel and you stay softYou're gonna punk out when we face off!
Rumble, they gon' take your face offThey gon' rumble, they gon' take your face offThey gon' rumble, they gon' take yo' face offThey gon' rumble, they gon' take yo' face offThey gon'
Barber cape off how I'm giving the fadeBased off the description I gaveCut the game off, I'm a street fighterBeat the breaks off like the whip on the stageHand out ass whippingsMy plate haters ate off, yeah, get hitler a steakIt'll be chaos like an emerald chaseWhen I face off in a nicolas cageY-y-y-yeah, I'm living in ragePunch a pussy nigga like I'm fisting his babeI was sitting in the 'spital, going mental, doc had told meThey'll forget me but they didn't, I remember them daysAnd I don't wanna be batman, nigga, I'm baneLiterally how the venom enters my veinsSince a little one, killa really been sick in the brainAnd the more I think about it, my life was twisted, waitReminiscing on when mom and dad would wonderWhy they couldn't reach me like they didn't have my numberFighting for my sanity, I never had the hungerPut a nigga underground quick, that's a bunkerSh-shock 'em like I'm master's brotherBetter pick your battles, busters halfway underHit more times than acupunctureWhen it comes to rumbling, I'm afro thunderReally it be funny when I kick it to you dummiesHow I'm grippin' on the iron when I'm rhyming on the micBecause it really will get bloody like I'm kicking it with buddiesWhen I beat a nigga up like I was iron nigga mikeI tell 'em they don't wanna tussle, when I'm at 'em, they gon' dropBet you that he stumble when I jab him then I crossLeave a nigga humble like, damn, when Kenny dropped'Cause really we can rumble like jackie in the bronxWe gon'
It's about drive, it's about power, we stay hungry, we devourPut in the work, put in the hours and take what's oursBlack and samoan in my veins, my culture bangin' with strangeI change the game so what's my motherfuckin' name? (rock)What they gonna get though?Desecration, defamation, if you wanna bring it to the massesFace to face now we escalatin' when I have to put boots to assesMean on ya like a dream when I'm rumblin'You're gonna scream, mamaSo bring drama to the king brahma (then what?)Comin' at ya' with extreme mana (ahoo, ahoo, ahoo)
When there is a stoppage in play, Face-off Probability generates predictions for who will win the upcoming face-off based on the players on the ice, the location of the face-off, and the current game situation. The predictions are generated throughout the stoppage until the game clock starts running again. Predictions occur at sub-second latency and are triggered any time there is a change in the players involved in the face-off.
Predicting the probability of a player winning a face-off in real time on a televised broadcast has several technical challenges that had to be overcome. These included determining the features required and modeling methods to predict an event that has a large amount of uncertainty, and determining how to use streaming PPT sensor data to identify where a face-off is occurring, the players involved, and the probability of each player winning the face-off, all within hundreds of milliseconds.
Determining the correct cut-off distance for proximity to a face-off location and the corresponding cut-off velocity for stationary players was accomplished using a decision tree model. With PPT data from the 2020-2021 season, we built a model to predict the likelihood that a face-off is occurring at a specified location given the average distance of each team to the location and the velocities of the players. The decision tree provided the cut-offs for each metric, which we included as rules-based logic in the streaming application.
With the correct face-off location determined, the player from each team taking the face-off was calculated by taking the player closest to the known location from each team. This provided the application with the flexibility to identify the correct players while also being able to adjust to a new player having to take a face-off if a current player is waived out due to an infraction. Making and updating the prediction for the correct player was a key focus for the real-time usability of the model in broadcasts, which we describe further in the next section.
To develop the model, we used more than 200,000 historical face-off data points, along with the custom engineered feature set designed by working with the subject matter experts. We looked at features like in-game situations, historical performance of the players taking the face-off, player-specific characteristics, and head-to-head performances of the players taking the face-off, both in the current season and for their careers. Collectively, this resulted in over 100 features created using a combination of available and derived techniques.
To evaluate the performance of the models, we used multiple techniques. We used a test set that the trained model was never exposed to. Additionally, the teams conducted extensive manual assessments of the results, looking at edge cases and trying to understand the nuances of how the model looked to determine why a certain player should have won or lost a face-off event.
One of the goals of the project was not just to predict the winner of the face-off, but to build a foundation for solving a number of similar problems in a real-time and cost-efficient way. That goal helped determine which components to use in the final architecture.
The third crucial component is Amazon SageMaker. Although we used SageMaker to build a model, we also needed to make a decision at the early stages of the project: should scoring be implemented inside the face-off detecting application itself and complicate the implementation, or should the face-off detecting application call SageMaker remotely and sacrifice some latency due to communication over the network? To make an informed decision, we performed a series of benchmarks to verify SageMaker latency and scalability, and validated that average latency was less than 100 milliseconds under the load, which was within our expectations.
With the main parts of high-level architecture decided, we started to work on the internal design of the face-off detecting application. A computation model of the application is depicted in the following diagram.
The compute model of the face-off detecting application can be modeled as a simple finite-state machine, where each incoming message transitions the system from one state to another while performing some computation along with that transition. The application maintains several data structures to keep track of the following:
Another optimization technique that we used was grouping requests to SageMaker and performing them asynchronously in parallel. For example, if we have four new combinations of face-off parameters for which we need to get predictions from SageMaker, we know that each request will take less than 100 milliseconds. If we perform each request synchronously one by one, the total response time will be under 400 milliseconds. But if we group all four requests, submit them asynchronously, and wait for the result for the entire group before moving forward, we effectively parallelize requests and the total response time will be under 100 milliseconds, just like for only one request.
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Double click on your clip to enlarge the drawer. Inside the drawer, you will find a host of emojis along with blur face effects. Select one as you wish, and your chosen emoji will be automatically placed on the moving face, as is detected by the Filmora9 software.
When working with the Face-Off effect, Light and Focus are really important to have on your clip. You need a high focus on your subject, for that it might not be fruitful to work with group videos. Another thing is light. If your video is too dark, then the effect might not be able to detect the face.
Stages are slightly more detailed, with some improved texturing and additional light sources sprucing up various scenes, and the lighting model itself has been mildly expanded with a heavy increase in bloom and a few more reflective effects on some surfaces. Contact flashes on the characters are more pronounced, and we also have more in the way of particles being kicked up as characters are struck or fall to the ground. 2ff7e9595c