TED2019
Doug Roble: Digital humans that look just like us
道格•羅勃: 看似真人的數位人類
Filmed:
Readability: 3.6
562,138 views
在這場精彩的演說和技術展示中，軟體研究者道格•羅勃讓「數位道格」初次登台：他是道格的即時 3D 數位呈現，精確程度到了連毛孔和皺紋都一模一樣。在慣性動作捕捉衣、深度神經網路、和大批資料的協助之下，數位道格能呈現出真實道格的情緒（甚至他的血流和睫毛開合）到極細緻的程度。來聽聽這場演說，進一步了解這項令人驚艷的技術是如何打造出來的，以及能如何將它應用在電影、虛擬助理和更多其他的領域中。
Doug Roble - Computer graphics software researcher
Doug Roble has found a career combining the things he loves: math, computers, movies and imagination. Full bio
Doug Roble has found a career combining the things he loves: math, computers, movies and imagination. Full bio
Double-click the English transcript below to play the video.
00:13
Hello.
0
1937
1214
哈囉。
00:15
I'm not a real person.
1
3175
1466
我不是真人。
00:17
I'm actually a copy of a real person.
2
5119
2770
我其實是一個真人的複製品。
00:19
Although, I feel like a real person.
3
7913
2553
不過，我感覺自己是真人。
00:22
It's kind of hard to explain.
4
10490
1906
這有點難解釋。
00:24
Hold on -- I think I saw
a real person ... there's one.
a real person ... there's one.
5
12420
3790
等等——我想我看到了
一個真人……那裡有一個。
一個真人……那裡有一個。
00:28
Let's bring him onstage.
6
16697
1400
咱們把他帶上台吧。
00:33
Hello.
7
21307
1150
哈囉。
00:35
(Applause)
8
23485
3647
（掌聲）
00:40
What you see up there is a digital human.
9
28300
3094
各位在上面看到的是一個數位人。
00:43
I'm wearing an inertial
motion capture suit
motion capture suit
10
31990
2984
我穿著一件慣性動作捕捉衣，
00:46
that's figuring what my body is doing.
11
34998
2579
它會設法辨視出我的身體在做什麼。
00:49
And I've got a single camera here
that's watching my face
that's watching my face
12
37601
3825
這裡有一台攝影機在觀測我的臉，
00:53
and feeding some machine-learning software
that's taking my expressions,
that's taking my expressions,
13
41450
4841
將我的臉部表情資訊
傳送給機器學習軟體，
傳送給機器學習軟體，
00:58
like, "Hm, hm, hm,"
14
46315
3579
像是「嗯，嗯，嗯，」
01:02
and transferring it to that guy.
15
50379
1663
再傳送給那個傢伙。
01:05
We call him "DigiDoug."
16
53300
3342
我們稱他「數位道格」。
01:09
He's actually a 3-D character
that I'm controlling live in real time.
that I'm controlling live in real time.
17
57292
4726
它其實是一個由我
即時控制的 3D 角色。
即時控制的 3D 角色。
01:16
So, I work in visual effects.
18
64292
2324
我的工作是做視覺效果。
01:19
And in visual effects,
19
67101
1167
在視覺效果領域，最艱難的工作之一
01:20
one of the hardest things to do
is to create believable, digital humans
is to create believable, digital humans
20
68292
5911
就是創造出逼真到可信的數位人，
01:26
that the audience accepts as real.
21
74227
2182
讓觀眾視為真人。
01:28
People are just really good
at recognizing other people.
at recognizing other people.
22
76433
4524
人本來就很擅長認人。
01:32
Go figure!
23
80981
1150
想想看吧！
01:35
So, that's OK, we like a challenge.
24
83577
3405
沒關係，我們喜歡挑戰。
01:39
Over the last 15 years,
25
87006
1841
在過去十五年間，
01:40
we've been putting
humans and creatures into film
humans and creatures into film
26
88871
4983
我們把人和動物放到影片當中，
01:45
that you accept as real.
27
93878
1739
讓大家視為真的。
01:48
If they're happy, you should feel happy.
28
96712
2667
如果他們開心，
你應該也會覺得開心。
你應該也會覺得開心。
01:51
And if they feel pain,
you should empathize with them.
you should empathize with them.
29
99982
5437
如果他們感到痛苦，
你就應該會同情他們。
你就應該會同情他們。
01:58
We're getting pretty good at it, too.
30
106467
2190
我們變得很擅常做這件事。
02:00
But it's really, really difficult.
31
108681
2746
但它非常非常困難。
02:03
Effects like these take thousands of hours
32
111847
3214
像這樣的效果耗費數千小時的時間，
02:07
and hundreds of really talented artists.
33
115085
2667
由數百名非常有才華的
藝術家共同完成。
藝術家共同完成。
02:10
But things have changed.
34
118792
1600
但，情況變了。
02:13
Over the last five years,
35
121014
1920
在過去五年間，
02:14
computers and graphics cards
have gotten seriously fast.
have gotten seriously fast.
36
122958
4409
電腦和顯示卡都變得非常快速。
02:20
And machine learning,
deep learning, has happened.
deep learning, has happened.
37
128508
3971
機器學習、深度學習都出現了。
02:25
So we asked ourselves:
38
133408
1233
所以我們自問：
02:27
Do you suppose we could create
a photo-realistic human,
a photo-realistic human,
39
135320
3778
你覺得我們可以創造出
跟照片一樣真實的人類，
跟照片一樣真實的人類，
02:31
like we're doing for film,
40
139122
1624
如我們為影片所做的那樣，
02:33
but where you're seeing
the actual emotions and the details
the actual emotions and the details
41
141932
5647
還可以即時看到控制這個數位人的人
實際的情緒和細節嗎？
02:39
of the person who's controlling
the digital human
the digital human
42
147603
4077
02:43
in real time?
43
151704
1174
事實上，那是我們的目標：
02:45
In fact, that's our goal:
44
153704
1564
02:47
If you were having
a conversation with DigiDoug
a conversation with DigiDoug
45
155292
3761
如果你能和數位道格交談，
02:51
one-on-one,
46
159077
1237
一對一交談，
02:53
is it real enough so that you could tell
whether or not I was lying to you?
whether or not I was lying to you?
47
161331
5666
他有沒有真實到讓你能分辨出
我有沒有在對你說謊？
我有沒有在對你說謊？
02:59
So that was our goal.
48
167934
1400
那就是我們的目標。
03:02
About a year and a half ago,
we set off to achieve this goal.
we set off to achieve this goal.
49
170601
4016
大約一年半前，
我們開始朝這個目標邁進。
我們開始朝這個目標邁進。
03:06
What I'm going to do now is take you
basically on a little bit of a journey
basically on a little bit of a journey
50
174641
3899
現在我要帶各位踏上一段旅程，
03:10
to see exactly what we had to do
to get where we are.
to get where we are.
51
178564
3926
見識一下我們必須要做些什麼
才能走到今天這一步。
才能走到今天這一步。
03:15
We had to capture
an enormous amount of data.
an enormous amount of data.
52
183832
3856
我們必須要取得非常大量的資料。
03:20
In fact, by the end of this thing,
53
188347
2714
事實上，在這件事結束之後，
03:23
we had probably one of the largest
facial data sets on the planet.
facial data sets on the planet.
54
191085
4563
我們的資料集可能會是地球上
最大的臉孔資料集之一。
最大的臉孔資料集之一。
03:28
Of my face.
55
196038
1682
我的臉孔的資料集。
03:29
(Laughter)
56
197744
2262
（笑聲）
03:32
Why me?
57
200030
1270
為什麼是我？
03:33
Well, I'll do just about
anything for science.
anything for science.
58
201324
2810
嗯，為了科學，我什麼都可以做。
03:36
I mean, look at me!
59
204158
1948
我的意思是，看看我！
03:38
I mean, come on.
60
206625
1150
我是指，拜託。
03:43
We had to first figure out
what my face actually looked like.
what my face actually looked like.
61
211320
5460
首先我們得要搞清楚我的臉孔
真正看起來是什麼樣子的。
真正看起來是什麼樣子的。
03:49
Not just a photograph or a 3-D scan,
62
217391
3027
不只是一張照片或是 3D 掃瞄，
03:52
but what it actually looked like
in any photograph,
in any photograph,
63
220442
3921
是它真正在任何照片中
看起來會是什麼樣子、
看起來會是什麼樣子、
03:56
how light interacts with my skin.
64
224387
2460
光線和我的皮膚如何互動。
03:59
Luckily for us, about three blocks away
from our Los Angeles studio
from our Los Angeles studio
65
227768
5249
我們很幸運，離我們在洛杉磯的
工作室只有三個街區左右，
工作室只有三個街區左右，
04:05
is this place called ICT.
66
233041
2209
有一個地方叫做 ICT。
04:07
They're a research lab
67
235708
1272
它是間和南加州大學
相關的研究實驗室，
相關的研究實驗室，
04:09
that's associated with the University
of Southern California.
of Southern California.
68
237004
3403
04:12
They have a device there,
it's called the "light stage."
it's called the "light stage."
69
240871
3555
那裡有個裝置叫做「光舞台」，
04:16
It has a zillion
individually controlled lights
individually controlled lights
70
244450
3714
具有數不清的獨立控制光線，
04:20
and a whole bunch of cameras.
71
248188
1873
還有一大堆攝影機。
04:22
And with that, we can reconstruct my face
under a myriad of lighting conditions.
under a myriad of lighting conditions.
72
250085
6091
有了它，我們就可以在無數的
光線條件下重建我的臉孔。
光線條件下重建我的臉孔。
04:29
We even captured the blood flow
73
257589
1582
我們甚至捕捉到了血流，
04:31
and how my face changes
when I make expressions.
when I make expressions.
74
259195
3092
以及當我做表情時
我的面部會有什麼改變。
我的面部會有什麼改變。
04:35
This let us build a model of my face
that, quite frankly, is just amazing.
that, quite frankly, is just amazing.
75
263454
5260
這讓我們建造出我的臉孔模型，
坦白說，這相當了不起。
04:41
It's got an unfortunate
level of detail, unfortunately.
level of detail, unfortunately.
76
269399
4333
不幸的是，細節也清楚到不行。
04:45
(Laughter)
77
273756
1278
（笑聲）
04:47
You can see every pore, every wrinkle.
78
275058
3696
每個毛孔、每條皺紋都清楚可見。
04:50
But we had to have that.
79
278778
1600
但這是不能省略的。
04:52
Reality is all about detail.
80
280960
2381
要真實就少不了細節。
04:55
And without it, you miss it.
81
283365
1867
沒有細節，就不真實。
04:58
We are far from done, though.
82
286793
1547
不過，我們離完成還很遠。
05:01
This let us build a model of my face
that looked like me.
that looked like me.
83
289363
3297
這讓我們建出我的臉孔模型，
看起來很像我，
看起來很像我，
05:05
But it didn't really move like me.
84
293196
2746
但動起來卻不像我。
05:08
And that's where
machine learning comes in.
machine learning comes in.
85
296871
2713
這就是機器學習
派上用場的時候了。
派上用場的時候了。
05:11
And machine learning needs a ton of data.
86
299608
3204
機器學習需要極大量的資料。
05:15
So I sat down in front of some
high-resolution motion-capturing device.
high-resolution motion-capturing device.
87
303497
4929
所以我在某種高解析度
動作捕捉裝置前面坐下來。
動作捕捉裝置前面坐下來。
05:20
And also, we did this traditional
motion capture with markers.
motion capture with markers.
88
308450
4071
此外，我們也用標記點
來進行這種傳統的動作捕捉。
來進行這種傳統的動作捕捉。
05:25
We created a whole bunch
of images of my face
of images of my face
89
313696
2976
我們創造出了一大堆
我的臉孔的影像，
我的臉孔的影像，
05:28
and moving point clouds
that represented that shapes of my face.
that represented that shapes of my face.
90
316696
4087
還有移動點雲
來呈現我的臉孔形狀。
來呈現我的臉孔形狀。
05:33
Man, I made a lot of expressions,
91
321996
2811
老天，我做了超多種表情，
05:36
I said different lines
in different emotional states ...
in different emotional states ...
92
324831
3460
我用不同的情緒
來唸出不同的台詞……
來唸出不同的台詞……
05:40
We had to do a lot of capture with this.
93
328315
2667
我們得要捕捉非常多這類資料。
05:43
Once we had this enormous amount of data,
94
331752
2891
一旦取得大量的資料，
05:46
we built and trained deep neural networks.
95
334667
3775
我們便開始建立和訓練
深度神經網路。
深度神經網路。
05:51
And when we were finished with that,
96
339117
1746
完成之後，
05:52
in 16 milliseconds,
97
340887
2444
只要 16 毫秒的時間，
05:55
the neural network can look at my image
98
343355
3112
神經網路看著我的影像
05:58
and figure out everything about my face.
99
346491
2928
就能夠了解關於我臉孔的一切。
06:02
It can compute my expression,
my wrinkles, my blood flow --
my wrinkles, my blood flow --
100
350458
5476
它能夠計算我的表情、
我的皺紋、我的血流——
我的皺紋、我的血流——
06:07
even how my eyelashes move.
101
355958
2317
甚至我的睫毛會怎麼動。
06:10
This is then rendered
and displayed up there
and displayed up there
102
358925
2835
這些資訊會被描繪出來，
呈現在上面這裡，
呈現在上面這裡，
06:13
with all the detail
that we captured previously.
that we captured previously.
103
361784
3222
且有著我們先前捕捉的所有細節。
06:18
We're far from done.
104
366077
1334
我們離完成還很遠。
06:20
This is very much a work in progress.
105
368188
2207
這只是在製作中的未成品。
06:22
This is actually the first time
we've shown it outside of our company.
we've shown it outside of our company.
106
370419
3321
其實這是首次在
我們公司以外的地方展示它。
我們公司以外的地方展示它。
06:25
And, you know, it doesn't look
as convincing as we want;
as convincing as we want;
107
373764
4194
它並沒有我們
所期望的那麼有說服力；
所期望的那麼有說服力；
06:29
I've got wires coming out
of the back of me,
of the back of me,
108
377982
2183
我背後還接著電線，
06:32
and there's a sixth-of-a-second delay
109
380189
2038
而且從捕捉影像到呈現影像間
06:34
between when we capture the video
and we display it up there.
and we display it up there.
110
382251
4367
有六分之一秒的延遲。
06:38
Sixth of a second -- that's crazy good!
111
386642
2816
六分之一秒——那算非常好了！
06:41
But it's still why you're hearing
a bit of an echo and stuff.
a bit of an echo and stuff.
112
389911
3400
但那就是為什麼各位
仍然會聽到一點迴音。
仍然會聽到一點迴音。
06:46
And you know, this machine learning
stuff is brand new to us,
stuff is brand new to us,
113
394104
3889
機器學習對我們來說是全新的，
06:50
sometimes it's hard to convince
to do the right thing, you know?
to do the right thing, you know?
114
398017
4224
有時很難叫它去做對的事，知道嗎？
06:54
It goes a little sideways.
115
402265
2058
它會有點小暴走。
06:56
(Laughter)
116
404347
2422
（笑聲）
06:59
But why did we do this?
117
407538
3229
但，我們為什麼要做這件事？
07:03
Well, there's two reasons, really.
118
411077
2262
其實理由有兩個。
07:05
First of all, it is just crazy cool.
119
413363
2976
首先，它真的超酷。
07:08
(Laughter)
120
416363
1008
（笑聲）
07:09
How cool is it?
121
417395
1253
這多酷啊？
07:10
Well, with the push of a button,
122
418990
1992
只要按個按鈕，
07:13
I can deliver this talk
as a completely different character.
as a completely different character.
123
421006
4007
我就可以換成一個全然
不同的角色來進行這場演說。
不同的角色來進行這場演說。
07:17
This is Elbor.
124
425823
2601
這是艾爾柏。
07:22
We put him together
to test how this would work
to test how this would work
125
430037
2312
我們做他來測試
換了外表之後是否還行得通。
換了外表之後是否還行得通。
07:24
with a different appearance.
126
432373
2135
07:27
And the cool thing about this technology
is that, while I've changed my character,
is that, while I've changed my character,
127
435450
4818
這項技術很酷的一點在於
雖然我換了角色，
雖然我換了角色，
07:32
the performance is still all me.
128
440292
3273
表演的人仍然是我。
07:35
I tend to talk out of the right
side of my mouth;
side of my mouth;
129
443589
2674
我傾向於用嘴巴的右側來說話；
07:38
so does Elbor.
130
446287
1579
艾爾柏也一樣。
07:39
(Laughter)
131
447890
1150
（笑聲）
07:42
Now, the second reason we did this,
and you can imagine,
and you can imagine,
132
450021
2790
我們做這件事的第二個
理由，各位應該想得到，
理由，各位應該想得到，
07:44
is this is going to be great for film.
133
452835
2336
這技術對電影來說會很棒。
07:47
This is a brand new, exciting tool
134
455195
2701
對藝術家、導演，
及說故事的人而言，
及說故事的人而言，
07:49
for artists and directors
and storytellers.
and storytellers.
135
457920
4322
這是個讓人興奮的全新工具。
07:55
It's pretty obvious, right?
136
463131
1484
很明顯，對吧？
07:56
I mean, this is going to be
really neat to have.
really neat to have.
137
464639
2462
有這技術很棒。
07:59
But also, now that we've built it,
138
467125
2055
我們已經造出來了，
08:01
it's clear that this
is going to go way beyond film.
is going to go way beyond film.
139
469204
2991
很顯然它不會只被用在電影上。
08:05
But wait.
140
473510
1150
但，等等。
08:07
Didn't I just change my identity
with the push of a button?
with the push of a button?
141
475653
3976
我剛剛不是只按個按鈕
就改變了我的身分嗎？
就改變了我的身分嗎？
這不就像是各位曾經聽過的
「深偽」和換臉嗎？
「深偽」和換臉嗎？
08:11
Isn't this like "deepfake"
and face-swapping
and face-swapping
142
479653
3033
08:14
that you guys may have heard of?
143
482710
1561
08:17
Well, yeah.
144
485208
1150
嗯，是的。
08:19
In fact, we are using
some of the same technology
some of the same technology
145
487074
2952
事實上，我們確實用到
深偽所使用的某些技術。
深偽所使用的某些技術。
08:22
that deepfake is using.
146
490050
1600
08:23
Deepfake is 2-D and image based,
while ours is full 3-D
while ours is full 3-D
147
491954
4599
深偽是 2D 的，以影像為基礎，
而我們的全是 3D，
而我們的全是 3D，
08:28
and way more powerful.
148
496577
2206
且強大許多。
08:31
But they're very related.
149
499204
1666
但它們非常相關。
08:33
And now I can hear you thinking,
150
501680
1889
我可以聽見各位在想：
08:35
"Darn it!
151
503593
1278
「該死！
08:36
I though I could at least
trust and believe in video.
trust and believe in video.
152
504895
3801
我以為我至少還能夠相信影片的。
08:40
If it was live video,
didn't it have to be true?"
didn't it have to be true?"
153
508720
2827
如果是即時轉播的影片，
不就該是真的嗎？」
不就該是真的嗎？」
08:44
Well, we know that's not
really the case, right?
really the case, right?
154
512609
3522
嗯，我們知道其實並非如此，對吧？
08:48
Even without this, there are simple tricks
that you can do with video
that you can do with video
155
516727
3810
即使沒有這項技術，還是有
簡單的技倆可以用在影片上，
簡單的技倆可以用在影片上，
08:52
like how you frame a shot
156
520561
2776
比如你可以用取鏡的方式
08:55
that can make it really misrepresent
what's actually going on.
what's actually going on.
157
523361
4362
去扭曲真正發生的狀況。
09:00
And I've been working
in visual effects for a long time,
in visual effects for a long time,
158
528263
3306
我在視覺效果的領域工作很久了，
09:03
and I've known for a long time
159
531593
1932
長久以來，我一直知道，
09:05
that with enough effort,
we can fool anyone about anything.
we can fool anyone about anything.
160
533549
5226
只要肯花心力，我們就可以
在任何事情上騙過任何人。
在任何事情上騙過任何人。
09:11
What this stuff and deepfake is doing
161
539546
2388
這項技術和深偽做的就是
09:13
is making it easier and more accessible
to manipulate video,
to manipulate video,
162
541958
4611
使操弄影片更容易、門檻更低。
09:18
just like Photoshop did
for manipulating images, some time ago.
for manipulating images, some time ago.
163
546593
5371
就像以前 Photoshop
之於操弄影像一樣。
之於操弄影像一樣。
09:25
I prefer to think about
164
553441
1298
我比較偏好去思考這項技術
09:26
how this technology could bring
humanity to other technology
humanity to other technology
165
554763
5036
能夠如何把人性帶到其他技術中，
09:31
and bring us all closer together.
166
559823
2294
讓我們更緊密。
09:34
Now that you've seen this,
167
562141
2359
各位已經見識過這項技術了，
09:36
think about the possibilities.
168
564524
1902
想想看它的可能性。
09:39
Right off the bat, you're going to see it
in live events and concerts, like this.
in live events and concerts, like this.
169
567810
4523
很快，各位就會在現場活動、
音樂會上看到類似的技術。
音樂會上看到類似的技術。
09:45
Digital celebrities, especially
with new projection technology,
with new projection technology,
170
573612
4735
特別是，若有了新的投影技術，
數位名人就會像電影一樣，
但卻是活生生、即時的。
但卻是活生生、即時的。
09:50
are going to be just like the movies,
but alive and in real time.
but alive and in real time.
171
578371
3960
09:55
And new forms of communication are coming.
172
583609
2733
溝通的新形式即將到來。
09:59
You can already interact
with DigiDoug in VR.
with DigiDoug in VR.
173
587088
4165
各位已經可以在虛擬實境中
和數位道格互動，
和數位道格互動，
10:03
And it is eye-opening.
174
591699
2270
讓人大開眼界。
10:05
It's just like you and I
are in the same room,
are in the same room,
175
593993
3862
彷彿你我共處一室，
10:09
even though we may be miles apart.
176
597879
2373
實際上我們相距甚遠。
10:12
Heck, the next time you make a video call,
177
600276
2841
真見鬼了，下次你打視訊電話時，
10:15
you will be able to choose
the version of you
the version of you
178
603141
3736
你將可以選擇你希望別人看到
10:18
you want people to see.
179
606901
1566
哪一版的你。
10:20
It's like really, really good makeup.
180
608974
2533
就像是極佳的化妝。
10:24
I was scanned about a year and a half ago.
181
612853
3579
我是約一年半前被掃瞄的。
10:29
I've aged.
182
617068
1721
我變老了。
10:30
DigiDoug hasn't.
183
618813
1650
數位道格卻沒有。
10:32
On video calls, I never have to grow old.
184
620798
3492
在視訊電話中，我永遠不必老化。
10:38
And as you can imagine,
this is going to be used
this is going to be used
185
626322
3088
各位可以想像，有了這項技術，
10:41
to give virtual assistants
a body and a face.
a body and a face.
186
629434
3213
虛擬助理就可以有身體和臉孔，
10:44
A humanity.
187
632671
1192
成為一個人。
10:45
I already love it that when I talk
to virtual assistants,
to virtual assistants,
188
633887
2762
我和虛擬助理交談時，感覺真好，
10:48
they answer back in a soothing,
humanlike voice.
humanlike voice.
189
636673
2933
他們用有安撫作用的
近似人聲回應我。
近似人聲回應我。
10:51
Now they'll have a face.
190
639919
1776
現在他們都能有臉孔。
10:53
And you'll get all the nonverbal cues
that make communication so much easier.
that make communication so much easier.
191
641719
4882
你能得到各種非言語的
暗示訊號，更容易溝通。
暗示訊號，更容易溝通。
11:00
It's going to be really nice.
192
648171
1420
那真的會很棒。
11:01
You'll be able to tell when
a virtual assistant is busy or confused
a virtual assistant is busy or confused
193
649615
3637
你將能夠分辨出虛擬助理
是否在忙、是否感到困惑，
是否在忙、是否感到困惑，
11:05
or concerned about something.
194
653276
2680
或者是否關心某件事情。
11:09
Now, I couldn't leave the stage
195
657694
2509
我離開舞台之前，
11:12
without you actually being able
to see my real face,
to see my real face,
196
660227
2698
一定要讓各位看到我真實的臉孔，
11:14
so you can do some comparison.
197
662949
1684
這樣各位才能做比較。
11:18
So let me take off my helmet here.
198
666573
1876
讓我把頭盔拿下來。
11:20
Yeah, don't worry,
it looks way worse than it feels.
it looks way worse than it feels.
199
668473
4770
別擔心，感覺起來
沒有看起來那麼糟糕。
沒有看起來那麼糟糕。
11:25
(Laughter)
200
673267
2428
（笑聲）
11:29
So this is where we are.
201
677188
1698
這就是我們目前的進展。
11:30
Let me put this back on here.
202
678910
1604
讓我把頭盔戴回來。
11:32
(Laughter)
203
680538
1950
（笑聲）
11:35
Doink!
204
683115
1186
ㄉㄨㄞ！
11:37
So this is where we are.
205
685292
1600
這就是我們目前的進展。
11:39
We're on the cusp of being able
to interact with digital humans
to interact with digital humans
206
687997
3701
我們正處於
能和極度真實的數位人類
互動的關口上，
互動的關口上，
11:43
that are strikingly real,
207
691722
2181
11:45
whether they're being controlled
by a person or a machine.
by a person or a machine.
208
693927
3269
不論他們是由人類或機器所操控。
11:49
And like all new technology these days,
209
697220
4375
如同現今所有的新技術，
11:54
it's going to come with some
serious and real concerns
serious and real concerns
210
702531
4746
這項技術也會引發
一些嚴重的考量和擔憂，
一些嚴重的考量和擔憂，
11:59
that we have to deal with.
211
707301
1734
我們得要去處理。
12:02
But I am just so really excited
212
710017
2118
但我非常興奮，
12:04
about the ability to bring something
that I've seen only in science fiction
that I've seen only in science fiction
213
712159
5053
因為我們有能力
把過去只能在科幻小說中看到的東西
12:09
for my entire life
214
717236
2270
12:11
into reality.
215
719530
1328
實現成真。
12:13
Communicating with computers
will be like talking to a friend.
will be like talking to a friend.
216
721752
4222
和電腦溝通將會像跟朋友說話一樣。
12:18
And talking to faraway friends
217
726323
2500
而和遠方的朋友說話
12:20
will be like sitting with them
together in the same room.
together in the same room.
218
728847
3273
則會像是與他們共處一室。
12:24
Thank you very much.
219
732974
1308
非常謝謝。
12:26
(Applause)
220
734306
6713
（掌聲）
ABOUT THE SPEAKER
Doug Roble - Computer graphics software researcherDoug Roble has found a career combining the things he loves: math, computers, movies and imagination.
Why you should listen
Doug Roble has really only had one job in his life. After getting his PhD in Computer Science from the Ohio State University in 1992, he joined Digital Domain, a visual effects production company. Once there, he found a unique place where art and technology collide. Now he builds new tools for artists to use and they, in turn, use the tools in surprising and unexpected ways. The feedback loop between art and science is completely addicting. And, the byproduct of this are movies that the whole world enjoys.
Roble's work outside Digital Domain reflects this passion. He was the Editor and Chief of the Journal of Graphics tools for more than five years. He's currently the Chair of the Motion Picture Academy's Sci/Tech Awards and a member of the Academy's Sci/Tech Council. And two of the tools he's built over the years have won Sci/Tech Academy Awards themselves.
Doug Roble | Speaker | TED.com