The .ACTR project is a 3D presentation standard for avatars. It is a method of generating real-time avatar animation from natural language generation models (NLP).
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
animations
documentation
examples
images
models
videos
LICENSE
README.md add devgarden info Sep 10, 2018

README.md

ACTR: Autonomous Character & Topology Resource

Animating Emotions by interpolating Fibonacci Chains and Iconic Gestures

What is ACTR

The ACTR project is a 3D presentation standard for avatars. It is a method of generating real-time avatar animation from natural language generation models (NLP). Check out the full documentation with tutorial here.

Introduction

Part of the SEED token project. This is a sneak preview - there is more to come. See the Wiki for more information.

About the SEED Token Project

SEED democratizes AI by offering an open and independent alternative to the monopolies of a few large corporations that currently control conversational user interfaces (CUIs) and AI technologies. SEED's licensed, monetized open-source platform for bots on blockchain supports collaboration and creative compensation that will exceed the proprietary deployments from industry giants. We are also giving users back control of their personal data. Find out more about the SEED Token project at seedtoken.io. See the Connect section at the end for contact info.

How to contribute - how to get involved

Go to our Developers Garden to see all featured projects, pick one and fill out the Developer Interest Form. If you rather like to discuss ideas before rolling up your sleeves, please come visit our Discord channels for developers

START HERE

Function

To drive realtime animations that are contextually appropriate to arbitrary natural language input or output. This may include animating an avatar based on the output of a natural language processing system, driving a VR scenegraph via voice control, or other purposes. Ultimately, ACTR generates realtime cinema from natural language strings. The following specifications detail animating avatars that are connected to chatbots.

The library preparation

We began with a breakout of 8 emotions (though this number can be different it has been convenient for us) and a single, nul / non-emotion. For a very similar model see Plutchik's wheel of emotions (which doesn’t work as well for reasons beyond our scope in this explanation). We’ll call these “emotion buckets.” We do not name the emotions as it is both unimportant and can even be detrimental to the development process.

Image 1

Image 2

We then built eight 33-second animations. Each of the eight animations cor- responded to the list of eight emotions. Two nul / non-emotion animations of the same duration were made, giving us a total of ten animations. Each of the 33 second animations were split into a Fibonacci sequence (1, 1, 2, 3, 5, 8, and 13 second durations). These were saved for later use, intended as a li- brary for later real-time concatenation during the conversation with the avatar. The link animation is then assigned a name based on these values, something like d13_e7 for emotion number seven at 13 seconds. This is in- tended as a reference for the player to then interpolate with other anima- tions.

The realtime operation

The NLP system produces a block of output text in human-readable format. In order to leverage the prepared ACTR library (above) we need a duration value and an affect value. Duration is the time it takes to speak that text and is calculated by assuming that one second equals 7 bytes. Second, the undetermined emotion (the affect or emotion of the output NLP text) is calculated by an external service (such as WordNet Affect, manually entered, or others) and the affect report is delivered to the PHP script that is monitoring MultiMedia tag generation. The script then pro- duces a control file that roughly matches that emotion and duration without having repeated animations happening adjacent to one another.

Generating the Chain Animation

A block of output text is evaluated so as to determine two values

  1. The duration report. Listed in seconds, this is based on the number of bytes if using a TTS system, or recording length, or whatever to deter- mine how long it takes to speak it.
  2. The affect report. Listed as an integer from 1-10, to correspond with our emotional model, that corresponds to the emotion number. These two values are then used to determine the duration and emotion of the composed, or chained, animation. This chained animation is a sequence of the links mentioned above generated by interpolating between the end-val- ues and start-values of successive link animations.
  • Note: This multimedia script simultaneously prevents recurring anima- tions. An example of what needs to be avoided is if we have two re- sponses that are each the same emotion and the same duration. So consider the possibility that the system produces a block of text that amounts to 9 seconds of emotion #2, then another block of text, on the subsequent output, that is also 9 seconds of emotion #2. We do not want the avatar to have precisely the same animation so the sys- tem might use 8+1/#2 to generate the first animation, but we do not want to run that same (8+1/#2) again, so we use instead anything but that. One option would then be 5+1+3/#2. This avoids repeats.

Iconic Gestures

At particular moments that require particular emphasis we use Iconic Ges- tures to break this up and bring attention to the words being said such that the Iconic Gesture matches the duration and sentiment of what is being said. ACTR provides 50 default iconic gestures (see below) however there is a spectrum of integration / customisation which is usually gated by scope

Notes

As well as driving the individual character animation ACTR may also be used:

  • To drive the entire scene graph (sun rises, flowers bloom, etc)
  • To drive camera direction (Whip-pan to CU, to Extreme CU, dolly etc). Used in cinematic techniques, this is key to building affective relationships with protagonists of stories, or other characters framed by the camera.
ACTR JSON Format Overview
The ACTR JSON format is designed to allow the communication of avatar con- trol data to an ACTR capable client for the purpose of rendering synchronized mouth movement, emotional expression, and iconic gestures.

Required animations:
  • for each of type = [head body]
  • 10 emotions labeled 1 through 10, 9 and 10 are alternate neutrals
  • durations (in milliseconds) 1000a, 1000b, 2000, 3000, 5000, 8000, 13000 for each emotion (there are two with duration = 1000)
  • result is 70 animations each for head and body
  • labels in the model are {type}_e{emotion}d{duration} or iconic{name} _d{duration}
  • e.g. head_e4_d1000a, body_e5_d2000, iconic_cheers_d4000 - standard iconics are: thumbsup, onethumbup, cheers, wave, etc
  • visemes
  • fall under "mouth" label
  • all characters must have a neutral mouth (e.g. mouth_neutral_d500)
  • all viseme animations are 500ms long with peak shape at 250ms to allow animation blending
  • each language will have a standard minimum set of visemes with its own phoneme to viseme mapping
Base English language visemes
o [o]
a [a]
c [cdgknrsy]
e [e]
f [fv]
m [m]
u [u]
neutral
Other details
  • each sequence is targeted to a specific scene element (i.e. character name)
  • each sequence is a combination of speech elements with head and body emotion animations
  • all the animations will be presented to fill the total duration
  • all sequences delivered in the same server response have the same start time
  • sequences with a start_delay must have their playback delayed by the indicated amount
  • speech visemes and entries playback is always synchronized
  • phoneme string is rendered in the International Phonetic Alphabet (http://en.wikipedia.org/wiki/International_Phonetic_Alphabet). See http://upodn.com/phon.asp for example transcodings.
  • viseme string is mapping of phoneme to available viseme animations
  • entries of different types (e.g. body and head) in the entries array are ren- dered in synchronization, according to order, from the start time

Elements

{ "sequence": {
	"element": string,		//name of the scene element that is
					// the target of this sequence
	"start_delay": integer,		//start time delay from "0" for this se-quence(millisecs)
	"total_duration": integer,	//duration of all animations together (mil-liseconds)
	"speech": string,		//Human readable speech text
	"phoneme": string,		//International Phonetic Alphabet version of speech
	"visemes": array of strings,	//array of viseme animation names 
					//mapped by server from phonemes
	"entries: [			//array of animation entries
		{
			"type": string,		//entry type [head|body|iconic]
			"label": string,	//label used in the model
			"description": string,	//human readable helper description
			"duration": float,	//duration of this entry in sec-onds.
			"order": integer,	//playback order of this entry 
						//(preserved within type)
			"info": {			//non-common fields, custom per type
				"type": string,		//should match parent type
				"emotion": string,	//emotion label, typically 1 through 10
				"duration": string, 	//duration label 
							// [1000a,1000b,2000,3000,5000,8000,13000]
				"mouth": float,		//used for custom mixing of mouth varia-ble
				"eyes": float		//used for custom mixing of eye variable
			}
		}
	]
	}
}

Full list of ACTR animations

Full list of ACTR animations

  • The character should be split into multiple FBX files per markup on the list

  • Each FBX file MUST use the same RIG

  • Each FBX file should contain one animation clip per named animation assigned to it

  • It is preferred that the animations are not set to have one keyframe per frame

  • durations are in milliseconds

  • @ 30 frames per second, the here are the mappings:

    • 500ms = 15F
    • 1000ms = 30F
    • 2000ms = 60F
    • 3000ms = 90F
    • 4000ms = 120F
    • 5000ms = 150F
    • 8000ms = 240F
    • 13000ms = 390F

Mouth entries are so short that ACTR will just use LERP to transition between them.

Emotions are defined according to the emotion wheel (see related image file). For head, emotions are essentially the various combinations of smile/frown with eyebrows up/down.

Here are the names of the emotions to map to the numbers

e1 - Mischievous
e2 - Happy
e3 - Kind/Sympathetic
e4 - Worried/Uncertain
e5 - Sad/Hurt
e6 - Disapproving/Disappointed
e7 - Angry
e8 - Determined
e9,10 - Neutral

Here is the full set of categories: e1,e2,e3,e4,e5,e6,e7,e8,e9,admiring,encouraging,appreciative,congratulatory,positive,cheers,wave,hello,goodbye,thumbsup,onethumbup,pout

NLP can send comma-delimited sets of the categories to be played back and ACTR will generate a playlist of the according length combining the different cat-egories.

e.g. affect=thumbsup,e2

Head emotions should also include blinking, etc. For body, emotions should include hand gestures, breathing, etc.

Neutral emotions e9 and e10 will be "loopable" on the base neutral keyframe and so do not need transitions. Transitions to emotions and transitions from emotions must be played before each emotion animation sequence. This is true for both head emotion tracks and body emotion tracks.

Head and body for a given emotion will start and end in the "emotion" state and so are loopable, but they require a transition to emotion animation and transition from emotion animation to be played before a given emotion sequence is played.

Iconics are also loopable, starting and ending on the "neutral" keyframe to avoid the need for specific iconic transition animations.

BELOW IS THE FULL SET OF EMOTION ANIMATIONS AND TRANSITIONS IN ORDER OF IM-PLEMENTATION PRIORITY.

Please NOTE the naming convention for the A and B variants on the 1 second ver-sions of each emotion.

Please NOTE the markup indicating which glTF/glb file should contain the animation.

<mouth_and_iconic.glb>

mouth_o_d500 mouth_a_d500 mouth_c_d500 mouth_e_d500 mouth_f_d500 mouth_m_d500 mouth_u_d500 mouth_neutral_d500

</mouth_and_iconic.glb>

<e9.glb>

head_e9-A_d1000 head_e9-B_d1000 head_e9_d2000 head_e9_d3000 head_e9_d5000 head_e9_d8000 head_e9_d13000

body_e9-A_d1000 body_e9-B_d1000 body_e9_d2000 body_e9_d3000 body_e9_d5000 body_e9_d8000 body_e9_d13000

</e9.glb>

<e2.glb>

head_transition-to-e2_d500 head_transition-from-e2_d500 body_transition-to-e2_d500 body_transition-from-e2_d500

head_e2-A_d1000 head_e2-B_d1000 head_e2_d2000 head_e2_d3000 head_e2_d5000 head_e2_d8000 head_e2_d13000

body_e2-A_d1000 body_e2-B_d1000 body_e2_d2000 body_e2_d3000 body_e2_d5000 body_e2_d8000 body_e2_d13000

</e2.glb>

<e10.glb>

head_e10-A_d1000 head_e10-B_d1000 head_e10_d2000 head_e10_d3000 head_e10_d5000 head_e10_d8000 head_e10_d13000

body_e10-A_d1000 body_e10-B_d1000 body_e10_d2000 body_e10_d3000 body_e10_d5000 body_e10_d8000 body_e10_d13000

</e10.glb>

<e3.glb>

head_transition-to-e3_d500 head_transition-from-e3_d500 body_transition-to-e3_d500 body_transition-from-e3_d500

head_e3-A_d1000 head_e3-B_d1000 head_e3_d2000 head_e3_d3000 head_e3_d5000 head_e3_d8000 head_e3_d13000

body_e3-A_d1000 body_e3-B_d1000 body_e3_d2000 body_e3_d3000 body_e3_d5000 body_e3_d8000 body_e3_d13000

</e3.glb>

<e4.glb>

head_transition-to-e4_d500 head_transition-from-e4_d500 body_transition-to-e4_d500 body_transition-from-e4_d500

head_e4-A_d1000 head_e4-B_d1000 head_e4_d2000 head_e4_d3000 head_e4_d5000 head_e4_d8000 head_e4_d13000

body_e4-A_d1000 body_e4-B_d1000 body_e4_d2000 body_e4_d3000 body_e4_d5000 body_e4_d8000 body_e4_d13000

</e4.glb>

<mouth_and_iconic.glb>

iconic_admiring-A_d1000 iconic_admiring-A_d2000 iconic_admiring-A_d3000 iconic_admiring-A_d4000 iconic_admiring-A_d5000 iconic_encouraging-A_d1000 iconic_encouraging-A_d2000 iconic_encouraging-A_d3000 iconic_encouraging-A_d4000 iconic_encouraging-A_d5000 iconic_appreciative-A_d1000 iconic_appreciative-A_d2000 iconic_appreciative-A_d3000 iconic_appreciative-A_d4000 iconic_appreciative-A_d5000 iconic_congratulatory-A_d1000 iconic_congratulatory-A_d2000 iconic_congratulatory-A_d3000 iconic_congratulatory-A_d4000 iconic_congratulatory-A_d5000 iconic_positive-A_d1000 iconic_positive-A_d2000 iconic_positive-A_d3000 iconic_positive-A_d4000 iconic_positive-A_d5000

</mouth_and_iconic.glb>

<e6.glb>

head_transition-to-e6_d500 head_transition-from-e6_d500 body_transition-to-e6_d500 body_transition-from-e6_d500

head_e6-A_d1000 head_e6-B_d1000 head_e6_d2000 head_e6_d3000 head_e6_d5000 head_e6_d8000 head_e6_d13000

body_e6-A_d1000 body_e6-B_d1000 body_e6_d2000 body_e6_d3000 body_e6_d5000 body_e6_d8000 body_e6_d13000

</e6.glb>

<e5.glb>

head_transition-to-e5_d500 head_transition-from-e5_d500 body_transition-to-e5_d500 body_transition-from-e5_d500

head_e5-A_d1000 head_e5-B_d1000 head_e5_d2000 head_e5_d3000 head_e5_d5000 head_e5_d8000 head_e5_d13000

body_e5-A_d1000 body_e5-B_d1000 body_e5_d2000 body_e5_d3000 body_e5_d5000 body_e5_d8000 body_e5_d13000

</e5.glb>

<e1.glb>

head_transition-to-e1_d500 head_transition-from-e1_d500 body_transition-to-e1_d500 body_transition-from-e1_d500

head_e1-A_d1000 head_e1-B_d1000 head_e1_d2000 head_e1_d3000 head_e1_d5000 head_e1_d8000 head_e1_d13000

body_e1-A_d1000 body_e1-B_d1000 body_e1_d2000 body_e1_d3000 body_e1_d5000 body_e1_d8000 body_e1_d13000

</e1.glb>

<e8.glb>

head_transition-to-e8_d500 head_transition-from-e9_d500 body_transition-to-e8_d500 body_transition-from-e9_d500

head_e8-A_d1000 head_e8-B_d1000 head_e8_d2000 head_e8_d3000 head_e8_d5000 head_e8_d8000 head_e8_d13000

body_e8-A_d1000 body_e8-B_d1000 body_e8_d2000 body_e8_d3000 body_e8_d5000 body_e8_d8000 body_e8_d13000

</e8.glb>

<e7.glb>

head_transition-to-e7_d500 head_transition-from-e7_d500 body_transition-to-e7_d500 body_transition-from-e7_d500

head_e7-A_d1000 head_e7-B_d1000 head_e7_d2000 head_e7_d3000 head_e7_d5000 head_e7_d8000 head_e7_d13000

body_e7-A_d1000 body_e7-B_d1000 body_e7_d2000 body_e7_d3000 body_e7_d5000 body_e7_d8000 body_e7_d13000

<mouth_and_iconic.glb>

iconic_cheers_d3000 iconic_wave_d3000 iconic_hello_d3000 iconic_goodbye_d3000 iconic_thumbsup_d3000 iconic_onethumbup_d3000 iconic_pout_d3000

</mouth_and_iconic.glb>

Iconic Gestures (Default List)

//Iconics are also loopable, starting and ending on the "neutral" keyframe to avoid the //need for specific iconic transition animations iconic_cheers_d1000 iconic_wave_d1000 iconic_hello_d1000 iconic_goodbye_d1000 iconic_thumbsup_d1000 iconic_onethumbup_d1000 iconic_pout_d1000

# User Says
(NLP_IN)
Gesture Description Gesture Name / ID
1 (idiosyncratic) iconic_interestedA_d1000
2 (idiosyncratic) iconic_interestedB_d2000
3 (idiosyncratic) iconic_interestedC_d3000
4 (idiosyncratic) iconic_interestedD_d4000
5 (idiosyncratic) iconic_interestedE_d5000
6 head cocked to left iconic_thinkingA_d1000
7 chin raised, eyes narrowed iconic_thinkingB_d2000
8 chin lowered, looks at user iconic_thinkingC_d3000
9 holds chin in right hand, folds left arm across abdomen iconic_thinkingD_d4000
10 rests chin on right fist, folds left arm across abdomen iconic_thinkingE_d5000
11 Eyes widen/eyebrows raise iconic_surprisedA_d1000
12 (idiosyncratic) iconic_surprisedB_d2000
13 (idiosyncratic) iconic_surprisedC_d3000
14 (idiosyncratic) iconic_surprisedD_d4000
15 (idiosyncratic) iconic_surprisedE_d5000
16 Right finger to chin iconic_listeningA_d1000
17 Right finger to right cheek iconic_listeningB_d2000
18 Eyes squint, nod iconic_listeningC_d3000
19 one eyebrow raised iconic_listeningD_d4000
20 head cocked to right iconic_listeningE_d5000
21 Puts hands behind body and nods head iconic_concernedA_d1000
22 Cocks head, chin moving to left, and furrows forehead iconic_concernedB_d2000
23 Holds chin in left hand, folds right arm across abdomen, left elbow resting on top of right hand iconic_concernedC_d3000
24 Places left palm across left cheek, folds right arm across abdomen iconic_concernedD_d4000
25 Jut chin forward and up, furrow brow iconic_concernedE_d5000

Prioritisation (top ten in order of most useful down with an evenly scattered range of duration)

  1. iconic_listeningA_d1000
  2. iconic_listeningB_d2000
  3. iconic_listeningC_d3000
  4. iconic_listeningD_d4000
  5. iconic_listeningE_d5000
  6. iconic_thinkingA_d1000
  7. iconic_thinkingB_d2000
  8. iconic_thinkingC_d3000
  9. iconic_thinkingD_d4000
  10. iconic_thinkingE_d5000
# Bot Says (NLP_OUT) Gesture Description Gesture Name
1 Eyebrows raise, small smile iconic_admiringA_d1000
2 Smile, nod head iconic_admiringB_d2000
3 Eyebrows raise, head swivel so that chin moves down and to the left (head cock) iconic_admiringC_d3000
4 Holds up right hand index finger, points at user (You!) iconic_admiringD_d4000
5 Thumbs up, right hand iconic_admiringE_d5000
6 (idiosyncratic) iconic_encouragingA_d1000
7 (idiosyncratic) iconic_encouragingB_d2000
8 (idiosyncratic) iconic_encouragingC_d3000
9 Nods head iconic_encouragingD_d4000
10 Folds hands in front of body and nods head iconic_encouragingE_d5000
11 Thumbs up, right hand iconic_appreciativeA_d1000
12 OK sign, right hand iconic_appreciativeB_d2000
13 (idiosyncratic) iconic_appreciativeC_d3000
14 (idiosyncratic) iconic_appreciativeD_d4000
15 (idiosyncratic) iconic_appreciativeE_d5000
16 Thumbs up iconic_congratulatoryA_d1000
17 Smile, thumbs up iconic_congratulatoryB_d2000
18 Claps (five beats) iconic_congratulatoryC_d3000
19 Nod, smile, thumbs up iconic_congratulatoryD_d4000
20 Fist pump in the air, smile iconic_congratulatoryE_d5000
21 Nod head iconic_positiveA_d1000
22 Close eyes, nod head two beats, open eyes iconic_positiveB_d2000
23 (idiosyncratic) iconic_positiveC_d3000
24 (idiosyncratic) iconic_positiveD_d4000
25 (idiosyncratic) iconic_positiveE_d5000

Prioritisation (top ten in order of most useful down with a mostly-evenly scat-tered range of duration)

  1. iconic_admiringA_d1000
  2. iconic_admiringB_d2000
  3. iconic_admiringC_d3000
  4. iconic_admiringD_d4000
  5. iconic_admiringE_d5000
  6. iconic_appreciativeA_d1000
  7. iconic_appreciativeB_d2000
  8. iconic_positiveA_d1000
  9. iconic_positiveB_d2000 10 iconic_encouragingD_d4000

Miscellaneous Notes

###Prioritization of Affect In 100 outputs the avatar will be neutral (#9 & #10) and happy (#2) the majority of the time. Generally speaking the left side of the chart is less commonly used, so the avatar is rarely seen with its eyebrows down, and even less commonly seen with both eyebrows and mouth down.

Group Prio
#9 20%
#2 20%
#10 20% = 60% of animations
#3 12%
#4 9%
#6 6%
#5 6%
#1 3%
#8 2%
#7 2%

Disclaimer

These files are made available to you on an as-is and restricted basis, and may only be redistributed or sold to any third party as expressly indicated in the Terms of Use for Seed Vault.

APPENDIX I

Patents and related intellectual property (full applications available upon request)

SYSTEMS AND METHODS FOR AN AUTONOMOUS AVATAR DRIVER;

  • Application #14/536,626
  • Means of using NLP and identification methods to provide a visual representa-tion of an avatar to talk and perform in-world (virtual) functions.

SYSTEMS AND METHODS FOR CINEMATIC DIRECTION AND DYNAMIC CHARACTER CON-TROL VIA NATURAL LANGUAGE OUTPUT

  • Application #15/49,164
  • Driving realtime 3D graphics with linguistic (NLG) output, this is a method of turn-ing text into a cinematic sequence of animations, such as animating an avatar and an associated scenegraph.

Image 3

Image 4

Connect

Feel free to throw general questions regarding SEED and what to expect in the following months here on GitHub at @consiliera (gaby@seedtoken.io) ☀️

Connect with us elsewhere

Seed Vault Code (c) Botanic Technologies, Inc. Used under license.