80 Level Podcast

Plask AI at Gamescom 2022: Revolution in Animation - 80 Level Podcast

Kirill Tokarev / Junho Lee Season 2 Episode 11

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We've talked with the CEO of Plask AI Junho Lee about changing traditional animation workflows with modern neural network solutions. Plask is an all-in-one browser-based AI motion capture tool and animation editor.

Plask Website: https://plask.ai/
Try Plask for free: https://80lv.pro/plasktrial 

Junho Lee is the Founder & CEO of Plask
LinkedIn: https://www.linkedin.com/in/junho-lee-a20419162/

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0:00

gamescom


0:01

hasn't been done for a couple of years


0:03

since covet and this was the first time


0:06

we kind of went


0:07

and met all our friends and tried to


0:09

make new connections at the recently


0:12

opened event


0:13

it was great meeting everybody and it


0:15

was even more exciting to kind of


0:17

explore the show floor


0:19

uh for the first time in two years and


0:22

we've seen a lot of uh returning


0:24

pavilions like there were a lot of


0:26

spaces where you could find you know


0:28

companies from a particular area in


0:31

europe like you know spain or area and


0:34

asia like malaysia


0:35

and some other spaces there were a lot


0:37

of publishers as well uh there were not


0:40

that many big publishers out there but


0:42

it was still interesting to see a lot of


0:45

local european houses kind of providing


0:47

and showing the games they're building


0:49

we haven't seen that many tools and tool


0:52

developers at gamescom seems like this


0:56

is not the kind of event that developers


0:58

of new technology for game production


1:00

usually like to attend and a lot of them


1:04

actually they don't have they do attend


1:06

but they don't have boost there but


1:07

there was one company that we were very


1:10

fortunate to have a little


1:12

time with


1:14

and


1:15

talk about


1:16

the future of video game production and


1:19

the future of animation


1:21

in particular


1:22

this company is plask


1:24

and i'm very happy to present our


1:26

interview with the ceo of this amazing


1:29

new startup


1:30

greetings and welcome to the 80 level


1:32

roundtable podcast in each episode host


1:36

corel tokorev invites video game


1:38

industry leaders to talk about the world


1:40

of game development


1:42

no topic is off limits as long as it


1:45

relates to video game development


1:47

new episodes are in the works so


1:49

remember to follow us or subscribe and


1:51

share with someone you know will also


1:53

enjoy the podcast so


1:57

before we kind of start


2:00

we ask this question a lot to our uh the


2:02

people that we're just talking with


2:04

do you actually play games


2:07

oh that's very um important question so


2:10

yes


2:11

yes but like i'm quite maniac i would


2:14

say because you know um


2:17

i'm not quite sure you know that but in


2:20

korea most of people play either


2:22

starcraft or


2:24

league of legends


2:25

so i'm not kind of the mainstream gamer


2:28

in queen


2:29

society but i like to play the


2:32

civilization or like oh really yeah


2:34

those kind of jump some boring games i


2:36

like those kind of things like this is


2:38

weird but a lot of people


2:41

who are doing business


2:43

in tech


2:44

they're really into civilization oh i


2:47

think yeah i think that's not a


2:49

coincidence yeah yes that's not a


2:51

coincidence like i think mark zuckerberg


2:53

he's into civilization


2:55

the boss of our company he's also a big


2:58

fan of civilization yeah and it seems


3:00

like everyone who's like


3:01

like to manage and figure those things


3:04

out they yeah even playing games


3:06

we'd like to manage but um


3:11

kind of what brought you to gamescom


3:13

this year what are you showcasing at


3:15

this show


3:16

yeah i am i can say that i'm a gamer but


3:20

the game uh the reason coming to


3:22

gamescom is not kind of poor like really


3:24

to enjoy the game industry but


3:27

it's more like the business


3:28

purpose because you know flask have been


3:31

on a great infrastructure for game


3:34

companies and productions and


3:36

we know that we can make the


3:38

productivity of the game industry really


3:41

really improved by


3:42

um


3:44

our service so i think we think it's a


3:47

great opportunity to introduce our tool


3:49

to the industry and show what we're


3:52

doing right now yeah that's i think the


3:54

main purpose


3:56

so tell us a little bit about your


3:58

product what does uh flask do what kind


4:01

of products do you provide


4:03

a little bit about that direction um so


4:05

yeah flask is an ai motion capture


4:08

[Music]


4:09

software we provide uh so you know


4:12

motion capture needs a lot of sensors


4:14

suits studios especially money and time


4:17

and class don't need any of those so it


4:20

is uh it only uses a video in order to


4:22

get a 3d motion data so


4:25

what you have to do is just


4:27

a prepare a video it can be video taken


4:29

from your phone or download it from


4:31

youtube it all works


4:32

so you just can drag and drop the video


4:35

into our tool our tool is a


4:37

browser-based tool and no need for


4:39

installations so you just can drag and


4:40

drop video into the tool and you get the


4:42

3d motion data so


4:44

uh this makes the whole production much


4:47

much easier because you know animating


4:49

became the animation was uh


4:52

the most bottleneck of the production it


4:54

takes a lot of time a lot of effort but


4:57

just by dragging and drop and video and


5:00

getting up motion data is a really big


5:02

thing especially for like


5:04

the 3d games i mean the mmr pitch or


5:06

like big scale games they can save a lot


5:09

of time and money uh by using flask uh


5:12

for making their animation process much


5:14

more efficient


5:15

so


5:16

how does it work usually


5:18

when you think about motion capture like


5:20

you said you need to have a suit there


5:22

has to be cameras around right there has


5:24

to be trackers that are on you and this


5:26

allows for a very accurate


5:28

representation of motion yeah especially


5:31

when you think about


5:32

um you know facial animations or some


5:35

very complex motions when you're right


5:37

involving fingers or some other elements


5:40

those are super important how does


5:43

your technology work like what's the


5:45

secret there how does it know how to


5:48

track the


5:49

you know motion from the image which is


5:51

basically flat as far as in the exact


5:54

video so the previous method the motion


5:56

capture uh uses a optical or like


6:01

electromagnetic sensors in order to get


6:03

the exact position of its joints so you


6:07

would probably see that they put markers


6:09

on their suits so they are using the


6:12

cameras or the sensor setup in order to


6:15

get the position of that marker and


6:18

making it to recover into the motion


6:20

data that's how they used to do uh so


6:23

they are basically using um


6:26

the tracking sensor that's the main


6:28

difference we are using the ai


6:31

or like


6:32

uh computer vision technology to get uh


6:35

motion reconstructed from video so


6:37

it's like a human eye or human brain or


6:39

like human intelligence because


6:42

when we see a video we are not 100 sure


6:45

about the death data or like 100 sure


6:48

about edge of uh the position of each


6:50

joint but we somehow know


6:53

um


6:54

the


6:56

approximated uh position of each data


6:59

while you can see that someone come


7:02

closer to camera


7:05

or like going far from camera it all can


7:07

be reconstructed in human brain so


7:10

that's what ai do ai was trained by


7:13

3d motion data so they were trained to


7:16

estimate the


7:18

30 joints of


7:20

each joint of the person in the video by


7:22

looking at huge data set that's how our


7:25

ai reconstruct 3d motion data from video


7:28

and it allows it to


7:30

by not uh


7:32

getting a 3d depth data or like


7:35

sensor data it somehow


7:37

can get


7:39

reconstructed 3d positions so


7:43

when you think about this technology how


7:45

accurate


7:46

is the


7:47

traction of movement how accurate can


7:49

you go


7:50

so we should talk about uh the prison


7:53

state and how it can go beyond


7:57

so for now we can say that our


8:00

okay quality is like low budget motion


8:03

suits so compared to like high and


8:06

motion call motion capture studios yes


8:09

our motion data is a little bit


8:12

[Music]


8:14

noisy and like


8:15

uh many users are giving feedback that


8:18

the feeds are like


8:20

quite slightly


8:21

those are still we have still those have


8:24

problems but the fun part is that last


8:27

year


8:28

the ai


8:30

quality was like too awful that it isn't


8:33

like serviceable and two years ago


8:36

no one think about


8:38

to make this into service because you


8:40

know it was that terrible


8:42

and you know uh the growth of the ai


8:45

technology is really fast and we


8:47

estimate that most of current uh users


8:50

feedback i mean the fit sliding and


8:52

southern noise problems will be so


8:54

within four and five months and after


8:57

one year later i think uh we can


9:00

approach to the quality of


9:03

middle and uh


9:04

middle class motion capture suit and


9:07

like two or three years later you can go


9:11

like similar level with the high quality


9:13

motion capture studios


9:15

so that's the thing i guess um the


9:17

future of the motion capture should be


9:20

and


9:21

must be the ai motion should capture


9:23

because uh the performance will


9:26

uh finally converge into the high


9:28

quality market and um


9:31

but the


9:32

accessibility or the


9:35

um


9:36

the efficiency the amount gives to the


9:39

production is uncomparable so


9:42

yeah that's what we are estimating about


9:45

the performance


9:47

so


9:48

let's talk a little bit about how it


9:50

works so as far understand you're a


9:51

web-based tool right you upload some


9:53

kind of video there then the ai does its


9:56

magic and it looks at the


9:58

human positions and


10:00

what happens next does your ai also


10:03

create a rig for the character can you


10:06

then kind of export this rig to any


10:08

character that you have in the game and


10:10

kind of end the animation as well right


10:12

so yeah um


10:15

so when you uh drag and drop the video


10:17

tool you get the rotation data of 203


10:21

joints which is exactly same with the


10:23

mixed summary and what we are doing is


10:25

we support an automatic retargeting


10:28

feature so user just can drag and drop


10:30

their custom 3d model to of course issue


10:33

week


10:34

they re-character to our tool and you


10:37

just can re-target the


10:39

ai extracted motion to they can apply


10:42

that to


10:44

their custom 3d characters so no we are


10:46

not um creating a rig for a 3d custom


10:50

character but


10:51

the ai extracting motion can be applied


10:54

to any of your humanoid characters so


10:57

we can say that we have some uh kind of


11:00

comparability so and uh after you apply


11:03

the motion to the


11:05

character you can export it to


11:07

fbx glbbh so you can bring it to other


11:10

tools like unity blender any other tools


11:12

you like right so


11:14

you need a rigged character to kind of


11:17

you utilize the motion that the


11:20

flask extracts from the video and after


11:23

that you can use it as uh


11:25

as much as possible whatever you can


11:27

right what about the


11:29

editing part so


11:31

if i want to create


11:33

like in


11:34

let's take for example like modern


11:36

action games or any kind of games right


11:38

now


11:40

if you've seen like last of us part two


11:42

yeah they did like


11:44

like a billion different animations for


11:47

every kind of movement that the


11:48

character can do and that's why the


11:50

character can open a door


11:52

six


11:53

different ways right or and it's like


11:55

every time you watch it it's like a


11:57

different motion


11:59

it requires a lot of motion capture but


12:01

it also requires a lot of editing


12:03

because you need to cut this motion and


12:05

that motion and so on so basically with


12:08

your data when it's exported can you


12:10

just


12:12

export it to maya i guess and play


12:14

around there and cut it into like


12:16

whatever pieces you want that's really


12:18

good point because


12:19

um actually um our internal


12:22

goal or like internal target is like


12:24

two-way the first one is uh to


12:27

automating the animation industry with


12:30

ai that's the one big part and the next


12:33

big part is collaboration so


12:36

um we are adding more and more uh


12:38

editing features to the tool


12:41

and we choose a browser to be the


12:44

platform uh of our tool uh there are a


12:47

lot of reasons so


12:49

uh as you just told you as you just told


12:52

so


12:53

animating is kind of like the


12:57

work of art you know it is not our just


13:00

a technical job so there should be a lot


13:03

of editing jobs after even if they


13:06

capture a motion from a fancy motion


13:08

capture studio they have to edit the


13:10

motion to make it into the form of art


13:12

so um editing is like the one of the


13:15

essential part when it comes to game


13:17

animations and we want but


13:20

what people are doing right now is quite


13:22

an old way it's like the way just like


13:25

30 years ago and um we think there


13:29

should be about two things two


13:31

innovation should be brought to the


13:33

industry the first one is about the


13:34

collaboration so you know


13:38

figma is a great example to the ui ux


13:41

area they bring a real-time


13:42

collaboration to the ui ux design and


13:45

they make the whole


13:47

stage to the


13:49

browser and it makes a lot of people to


13:51

access uh the project and giving a


13:54

feedback working simultaneously and


13:57

making something into a


13:59

like


14:00

i am actually a a developer before i


14:03

start a


14:04

company and we developers


14:07

uh have some like very good culture i


14:10

would say it's like the like you know


14:12

there's a git top there and there's an


14:13

open source thing and there's


14:16

a collaboration between um total


14:18

strangers those kind of things should be


14:20

brought to the game


14:22

game design animation


14:24

as well i guess so that's why we are


14:27

adding more and more editing feature to


14:29

our browser-based tool and making it


14:30

into a real-time collaboration


14:32

uh or in uh


14:34

yeah i think those what


14:36

those are one of our great targets and


14:38

those kind of editing features that's


14:40

why we are adding more and more editing


14:42

feature to our tool yeah


14:45

so


14:45

i like this um analogy with sigma


14:48

because i think i use figma a lot


14:50

although i'm not a


14:52

designer but i do use it to kind of


14:53

review different forms and


14:56

it seems right that


14:58

kind of like sigma grew from just like a


15:01

ui


15:03

building tool to like more like a


15:05

photoshop kind of tool or like photoshop


15:08

plus coral draw because it's like vector


15:10

stuff and raster stuff and all of it


15:12

combined exactly and it's uh it is very


15:15

approachable so you can download it and


15:18

start playing around in the browser


15:20

basically without having to use


15:22

any other stuff


15:24

that you do with other comparable tools


15:27

um


15:28

like especially the 3d animation thing


15:31

the assets are too heavy and


15:34

the tool itself the maya the 3d max when


15:38

you mis-click the tool and it takes


15:41

on years to open up and yeah i think


15:44

those make the review and feedback


15:46

process really slow and


15:48

i think when it comes to browser it's


15:50

really really


15:52

fast accessible thing so i think it


15:54

would be great if you can bring it to


15:56

the industry


15:58

when i think about


15:59

[Music]


16:00

maya in particular


16:04

the rigs themselves


16:06

they can get pretty complex right right


16:09

and when we think about motion in


16:11

general


16:12

it is like like you said a very


16:14

complicated thing and that's why we also


16:17

run like a little bit of a recruiting


16:19

uh service and we see a lot of demand


16:22

for guys like technical animators or


16:24

animators in general because


16:27

like you said it's like you're sparkling


16:29

life


16:30

into a character certainly given motion


16:32

and making it sure that it moves


16:35

i feel like what you're saying about ai


16:38

could be


16:40

could bring out like the next level to


16:43

animation in general and the fluidity of


16:46

motion the versatility of motion because


16:49

right now everything is basically done


16:51

by hand i mean of course mocap helped a


16:55

lot


16:56

definitely took a lot of


16:58

pressure from animators but it's still a


17:00

lot of things that you need to do


17:03

for yourself or in teams and a lot of


17:05

cleaning like you said a lot of


17:07

you know


17:09

adapting the material and so on


17:11

when


17:13

we have ai a lot of those tasks are


17:15

being done by the machine


17:17

and my question is


17:19

what else do you feel in animation can


17:22

we outsource to computers so we don't


17:25

have to


17:26

think about it like


17:28

i think there is another piece of


17:30

software called the cascader or


17:32

something yeah they're doing a lot of


17:35

that like in that


17:36

it's basically like a keyframe approach


17:39

but aai kind of adds up and helps smooth


17:43

it out or right suggest motion or


17:47

help figure out when the impact is


17:49

happening what's going on with the


17:50

motion and the physics and all that


17:52

stuff


17:53

where do you think it's going to go from


17:55

here


17:56

how is plask kind of


17:58

going to push this ai into the animation


18:02

industry of course animation itself have


18:05

a lot of things to can be um automated


18:08

by ai because


18:10

i think at the end of


18:12

this world


18:13

maybe we can have um some kind of


18:15

animation features like


18:17

just like by using the natural language


18:19

maybe you can uh make a whole animation


18:22

so you are just like being a director so


18:25

like just move


18:26

in this way more or like just do it more


18:29

with a passion this can be be done with


18:32

ai because you know


18:34

similar things are done by the dali too


18:37

recently you know the natural language


18:39

can produce a


18:40

whole


18:41

artworks and


18:43

this can be applied to animations as


18:45

well so


18:46

those things so animation itself can be


18:48

really really


18:50

easy by using those kind of things but


18:52

not only for animation


18:54

so so example did a great job with uh


18:57

automatic rigging uh don't buy the


18:59

uh ai but you know rigging itself is


19:02

actually have much more


19:04

way to go than just mixamo because all


19:07

the deformation things and all the you


19:10

know that's why we have technical


19:12

artists in the game industry so i think


19:15

our next thing after we conquered the


19:17

animation job would be the


19:20

uh rigging thing and after that maybe we


19:22

can work with the building thing there


19:24

are a lot of things can be improved by


19:27

ai so


19:29

i think those kind of automations will


19:31

help


19:32

like i think this can be like youtube


19:35

thing so back then only videos were


19:38

mostly produced by the broadcasting


19:40

uh giants and normal people can make the


19:44

videos and shows but now everyone do


19:46

right


19:47

um i think after those kind of


19:50

animation after reading after modeling


19:53

after all the 3d pipeline productions


19:55

are automated i think really um everyone


19:58

would produce like


20:00

team of 10 make a


20:02

large-scale game and get


20:05

a like


20:06

hundreds millions of revenue i think


20:08

that would be possible after all the


20:12

um


20:13

bothering jobs will be automatically


20:15

automated do you feel like


20:18

uh adoption of ai tools will negatively


20:23

impact you know the workforce do you


20:25

think more people are going to be left


20:26

out of jobs


20:29

yeah that's very important question um


20:31

um no


20:33

because um


20:35

i think this kind of question was like


20:38

repeatedly when there's a new technology


20:41

coming to the industry and everyone uh


20:43

worrying about it replacing their jobs


20:46

but


20:47

what i found out is that


20:49

when


20:51

there is an ai coming to the industry


20:53

and replace the most


20:54

hard or like most repetitive jobs and


20:58

people just


21:00

move to more created jobs


21:02

our


21:03

goal is to


21:04

make every uh


21:06

members in the game team to focus on


21:09

their own creativity that's what we are


21:12

aiming for so like when ai makes the


21:15

whole draft by ai mocap then every


21:19

animators can now um work with the


21:22

sensitive the artistic or like the


21:25

uh craftsmanship thing to their emotions


21:28

and it can make their emotions like more


21:30

creative more sensitive more high


21:33

quality i think those will


21:36

yeah come to the area because you know


21:39

when there's like


21:43

yeah


21:44

so uh


21:45

final question how do we


21:47

uh start playing around with plus do we


21:49

just go online and click the link and


21:51

start working you don't need to


21:53

do you need to pay is there like a trial


21:55

period how does it work


21:57

um yeah you just visit flask.ai after


22:00

you make an account you can start with


22:03

plus right away we have a freemium plan


22:05

so you can have most of the features


22:08

with most of the credit uh most of those


22:10

you can use most of the feature without


22:12

any kind of payment method registration


22:15

but actually we are releasing first paid


22:17

version this weekend and it will have


22:20

some amazing explosive features uh like


22:24

uh previously we only can extract the


22:26

motion from a video with a one person


22:28

inside but uh with a newly coming paid


22:32

version that you can now


22:34

finally extract the motion of multiple


22:36

people in the video each of the motions


22:38

yeah those will be


22:41

will introduce the feature to the paid


22:43

version and there will be much more um


22:46

minor like


22:48

um features which which can make your uh


22:52

production much more effective so


22:54

maybe but the paid version will have the


22:56

free trial as well so you can try it out


22:59

and if you love it you can buy it but


23:02

our


23:03

vision is to provide but


23:05

our short-term vision is to introduce


23:07

the am or capture the industry that's


23:09

the first thing


23:10

more than a monetization so we will


23:12

focus on


23:14

more to provide or provide more ai-based


23:17

mocap without any cost so


23:20

maybe don't have to worry too much


23:23

all right thank you so much for your


23:24

interview


23:26

thanks for enjoying another episode of


23:28

the 80 level roundtable podcast


23:31

check out upcoming episodes on the 80


23:33

level website at 80.lv


23:36

join our career site at 80.lv


23:39

rfp


23:41

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23:43

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23:47

[Music]

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