D-24: Building the Future of AI Avatars - Technical Progress Report

The journey to create accessible, emotionally intelligent AI avatars has reached a significant milestone with our D-24 development phase. Our team has made substantial progress in bridging the gap between complex AI technology and user-friendly avatar creation, enabling anyone—regardless of technical background—to build and interact with their own AI companions through face-to-face conversational interfaces.

On April 30th, we will present a demo to our VCs, showcasing our method for cloning our mentor and instructor, Adam Paulisick. We believe this will effectively demonstrate our capabilities, as the VCs are already familiar with him.

Recent Technical Achievements

Pipeline Optimization and Benchmarking

Our recent focus has been on optimizing the entire pipeline for real-world deployment:

• API Performance: Completed comprehensive benchmark evaluations across all pipeline components
• Cloud Deployment: Successfully deployed models on AWS, achieving a notable 60x improvement in inference time by transitioning from CPU to GPU.
• Facial Animation Enhancement: Integrated SadTalker for sophisticated facial animation generation
• Emotional Expression: Utilizing GAN (GFPGAN) for enhanced facial expressions with deeper emotional depth
• Front-end development: Built the front-end website with fundamental features.

In the following section, we will showcase some videos highlighting our recent achievements.

Video Generation Showcase - Smooth Transition Between Video Responses

To ensure our avatars can engage in fully conversational interactions, we have to generate videos each time the avatar needs to speak and provide smooth transition between responses. We have developed a QA system that enables the avatar to respond dynamically, complemented by static reference animations. By utilizing neural network interpolation, we achieve smooth transitions between expressions, which require approximately 10 seconds of processing time.

Step 1. Static Reference Animation

First, our baseline static animations provide a foundation for consistent character presentation.

Step 2. Dynamic QA-Based Animation

This demonstration showcases our QA model’s ability to generate contextually appropriate facial animations based on real-time conversational input.

Step 3. Neural Network Interpolation

By using neural network interpolation, we achieve smooth transitions between expressions, which require approximately 10 seconds of processing time.

Video Generation Showcase - Lip Sync

To enable creators to design AI avatars with personalized voices, we developed functions that ensure the avatars can accurately synchronize lip movements with various audio inputs.

Backend Processing Visualization

Here’s an exclusive glimpse into our backend processing pipeline. At present, when creators submit their Gemini API Key, Avatar Image, and Voice Reference, we can generate a brief response that animates the image to simulate speech while matching the provided voice reference.

Technical Deep Dive

Current Technology Stack

1. Frontend Development:

   • Web-based interface for avatar creation and interaction
   • Real-time video rendering and display
   • Responsive design for multiple devices

2. Backend Processing:

   • Future work includes key frame sampling optimization for improved rendering speed
   • Future work includes exploring lightweight GAN models for efficient fine-tuning
   • Full-stack web service framework integration

3. AI Components:

   • Advanced facial animation generation
   • Emotional expression synthesis
   • Real-time conversation processing

Performance Metrics and Optimization

Current performance benchmarks show:

• GAN-generated frame rendering: 30 seconds with GPU
• Base rendering time: 10 seconds without enhancer
• Target response time: 3-5 seconds for real-time interaction
• Video concatenation processing: Variable based on content length

Technical Challenges and Solutions

Current Challenges

1. Rendering Speed:

   • Issue: 30-second rendering time for enhanced frames
   • Solution in Progress: Exploring lightweight GAN alternatives and optimized key frame sampling

2. Video Integration:

   • Challenge: Seamless concatenation of static and generated content
   • Approach: Developing smooth transition algorithms and buffer management

3. Real-time Interaction:

   • Goal: 3-5 second response time with emotional depth
   • Strategy: Pipeline optimization and parallel processing implementation

Ongoing Research and Development

Our team is actively working on:

1. Performance Optimization:

   • Investigating efficient video rendering techniques
   • Researching model fine-tuning approaches
   • Implementing parallel processing where applicable

2. User Interface Enhancement:

   • Designing intuitive controls for non-technical users
   • Developing real-time preview capabilities
   • Creating responsive feedback mechanisms

Next Development Phase

Immediate Goals

1. Component Integration:

   • Combining all pipeline elements into a cohesive system
   • Establishing robust communication between components
   • Implementing error handling and recovery

2. User Interface Development:

   • Creating an intuitive avatar creation interface
   • Implementing real-time interaction controls
   • Developing progress indicators and feedback systems

3. Performance Enhancement:

   • Optimizing video rendering pipeline
   • Improving model fine-tuning efficiency
   • Implementing caching and pre-rendering where appropriate

Looking Forward

The D-24 phase represents a significant step toward our goal of democratizing AI avatar creation. While we’ve made substantial progress in key areas like facial animation and emotional expression, we continue to push the boundaries of what’s possible in real-time AI interaction. Future work may include RAG-Optimization and further customization options.

We invite developers and enthusiasts to follow our progress and contribute to this exciting journey. Stay tuned for more updates as we move closer to our vision of accessible, emotionally intelligent AI companions.

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Note: This is a technical progress report. For user-friendly guides and general information about our AI companions, please visit our main product pages.