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Video lectures are present in abundance but the mocap data of those video lectures is 10 times ahead in the form of precise data. Despite the availability of such promising data, the problem of generating bone transforms from audio is extremely difficult, due in part to the technical challenge of mapping from a 1D signal to a 3D transform (translation, rotation, scale) float values, but also due to the fact that humans are extremely attuned to subtle details in expressing emotions; many previous attempts at simulating talking character have produced results that look uncanny( two company- neon, soul-machine). In addition to generating realistic results, this paper represents the first attempt to solve the audio speech to character bone transform prediction problem by analyzing a large corpus of mocap data of a single person.

What it does

Some of the cutting edge technologies like ML and DL have solved many problems of our society with far more better accuracy than an ideal human can ever do. We are using this tech to enhance our learning procedure in the education system.

The problem with every university student is, they have to pay a big amount of money for continuing to study at any college, they have to interact with the lecturers and professors to keep getting better and better. We are solving the problem of money.

 Our solution to this problem is, we have created here an e-text data to human AR character sparse point mapping machine learning model to replace the professors and use our ai bots to teach the same thing in a far more intractable and intuitive way that can be ever dome with the professors. The students can learn even by themselves AR characters too.

This project explores the opportunities of AI, deep learning for character animation, and control. Over the last 2 years, this project has become a modular and stable framework for data-driven character animation, including data processing, network training, and runtime control, developed in Unity3D / Unreal Engine-4/ Tensorflow / Pytorch. This project enables using neural networks for animating character locomotion, face sparse point movements, and character-scene interactions with objects and the environment. Further advances on this project will continue to be added to this pipeline.

Can customers afford it?

1. Individual
2. Student
3. Teacher
4. Institute
5. organization

Animating characters can be an easy or difficult task - interacting with objects is one of the latter. In this project, we present the Neural State Machine, a data-driven deep learning framework for character-scene interactions. The difficulty in such animations is that they require complex planning of periodic as well as aperiodic movements to complete a  given task. Creating them in a production-ready quality is not straightforward and often very time-consuming. Instead,  our system can synthesize different movements and scene interactions from motion capture data and allows the user to seamlessly control the character in real-time from simple control commands.

This is all a completely new feature.

Animating characters can be an easy or difficult task - interacting with objects is one of the latter. In this project, we present the Neural State Machine, a data-driven deep learning framework for character-scene interactions. The difficulty in such animations is that they require complex planning of periodic as well as aperiodic movements to complete a  given task. Creating them in a production-ready quality is not straightforward and often very time-consuming. Instead,  our system can synthesize different movements and scene interactions from motion capture data and allows the user to seamlessly control the character in real-time from simple control commands. Since our model directly learns from the geometry, the motions can naturally adapt to variations in the scene. We show that our system can generate a large variety of movements, including locomotion, sitting on chairs, carrying boxes, opening doors and avoiding obstacles, all from a single model. The model is responsive, compact and scalable, and is the first of such frameworks to handle scene interaction tasks for data-driven character animation.

This project explores the opportunities of AI, deep learning for character animation. Over the last 2 years, this project has become a modular and stable framework for data-driven character animation,  including data processing, network training, and runtime control, developed in Unity3D /  Unreal  Engine-4/ Tensorflow / Pytorch. This project enables using neural networks for animating character locomotion, face sparse point movements, and character-scene interactions with objects and the environment. Further advances on this project will continue to be added to this pipeline.  

We ues these specific hardware and softwares:

1) Mocap suite-  SmartSuite Pro from www.rokoko.com - single:  $2,495 + Extra   Textile-   $395

  2) GPU + CPU - $5,000

  3) Office premise – $ 2,000

  4) Data preprocessing

  5) Prerequisite software licenses- Unity3D, Unreal Engine-4.24, Maya,   Motionbuilder

  6) Model Building

  7) AWS Sagemaker and AWS Lambda inferencing

  8) Database Management System

Demo video explains everything: 

The market will include the entire education industry, information sharing, and explaining and internet mobile users.  

Is there enough demand?

Up to this time, no product like this has been launched in the whole of the world.  These are the works of cutting edge technologies related to machine learning and deep learning. There is a lot of research that is still running on these topics.  There's some kind of similar product that has been tried to create by Edureka but not in the efficient way that we are using here.

Financial Projections for the next 5 years

Date

Clients

COGS-$

Revenue-$

Net Cash Flow-$

Gross Margin->#/b###

31/06/2020

4,368

3,500

14,884

11,384

76.484

28/02/2021

6,412

5,000

22,549

17,549

77.826

31/06/2021

16,560

8,000

55,523

47,523

85.591

28/02/2022

35,600

20,000

135,000

115,000

85.185

31/06/2022

90,000

50,000

325,000

275,000

84.615

28/02/2023

200,000

95,000

700,500

605,500

86.5

31/06/2023

350,000

150,000

1,600,650

1,450,650

90.628

28/02/2024

1,000,000

400,000

5,100,000

4,700,000

92.156

31/06/2024

4,000,000

1,000,000

21,000,000

20,000,000

95.238

28/02/2025

10,000,000

2,500,000

45,000,000

42,500,000

94.444

31/06/2025

35,000,000

6,500,000

102,000,000

95,500,000

93.137

28/02/2026

90,000,000

17,000,000

450,000,000

433,000,000

96.222

Financial Support and founders

We are constantly working on it.