Technical Architecture and Technical Logic
1.1 Underlying Technical Architecture Design
Floa's core architecture is dual-driven by "Modular Microservices" and "Real-Time Digital Human Engine". Based on our self-developed digital human engine and private large model cluster, we focus on overcoming key challenges including real-time interaction of digital humans, secure rights confirmation of Web3 assets, and ecological expansion efficiency.
The overall technology stack is divided into five layers:
1.1.1 Infrastructure Layer: Ensuring Real-Time Interaction & Data Security
Computing Resources: Adopt a three-tier architecture of "Hybrid Cloud + Edge Nodes + Rendering Accelerators". To meet the real-time driving of digital humans' facial expressions, movements, and voices, we have deployed edge rendering nodes across multiple global regions, optimizing end-to-end interaction latency to less than 100ms.
Storage Solutions: Implement hierarchical processing based on data characteristics. Core assets (e.g., digital human avatars, motion libraries) are stored on IPFS with on-chain certification; training and interaction data use a distributed file system; we have specially designed a "Digital Asset Repository" to support high-concurrency, millisecond-level resource calls.
Security Mechanisms: Build a three-layer protection system—smart contracts undergo third-party audits, data transmission is encrypted throughout, and each digital human identity is uniquely authenticated and copyright-anchored via blockchain, ensuring Web3-native security for assets and data.
1.1.2 Core Technology Layer: Deep Integration of Large Models & Digital Human Engine
This layer serves as Floa's intelligent core, enabling a closed loop from "Perception" to "Generation" and then to "Decision-Making". We have conducted in-depth optimizations based on open-source architectures, with the key improvement lying in the collaborative reasoning efficiency between large models and the digital human engine.
Below is a code snippet illustrating our interaction logic.
python
# Core Interaction Engine: End-to-End Generation from Text to Digital Human Performance
class FLOAAgentCore:
def __init__(self, model_path: str, renderer_config: dict):
# Load the large model, using bfloat16 precision to balance performance and overhead
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
self.llm = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Initialize the digital human rendering engine
self.face_renderer = FaceRenderer(renderer_config["face"])
self.motion_controller = MotionController(renderer_config["motion"])
def generate_response(self, user_input: str) -> tuple:
# 1. Generate text responses (with integrated context management)
prompt = self._build_prompt(user_input)
response_text = self._generate_text(prompt)
# 2. Parallelly drive digital human performance (a key FLOA optimization)
emotion = self._predict_emotion(response_text) #Lightweight sentiment analysis
motion_sequence = self._generate_motion(emotion, response_text)
# 3. Compose rendering data streams
render_data = {
"facial": self.face_renderer.render(emotion),
"motion": self.motion_controller.execute(motion_sequence)
}
return response_text, render_dataOur Core Optimizations:
Model Collaboration: Collaborate with leading model teams to customize a "Digital Human Multimodal Enhancement Layer" on the foundation model. This layer unifies the reasoning of speech, semantic, and visual signals, significantly enhancing interaction naturalness.
Decision Engine: Integrate rule-based engines with RLHF strategies. It not only handles task planning but also dynamically adjusts digital humans' interactive performance (e.g., tone, microexpressions), synchronizing "task execution" and "emotional interaction".
Tool Framework: The built-in API gateway has integrated 30+ services, with a dedicated ecological open interface layer designed. In the future, via key management and rate limiting, we will safely open digital human capabilities to developers.
1.1.3 Agent Capability Layer: Scalable Digital Human Skill System
Basic Capabilities: Offer out-of-the-box voice/text conversation, real-time avatar driving, and basic task automation (e.g., schedule management, information retrieval).
Advanced Capabilities: Gradually unlocked through training, including multi-agent collaboration (virtual teams), commercial scenario customization (brand endorsement, virtual live streaming), and Web3 asset integration (NFT management, on-chain transactions).
Personalized Customization: Support full-dimensional customization from appearance (character modeling, outfits) and skills (model fine-tuning) to interaction styles (tone, expression preferences).
1.1.4 Ecological Interaction Layer: Multi-terminal & Cross-platform Adaptation
User Interfaces: Compatible with Web, mobile DApps, and VR/AR devices, ensuring consistent digital human rendering across terminals. Provide a low-code editor for users to quickly create exclusive digital humans.
Developer Interfaces: Gradually opened in phases: V1.5 (Basic Capability APIs) → V2.0 (Large Model Collaboration APIs) → V3.0 (Complete SDK & Developer Platform).
Cross-ecosystem Integration: Seamlessly integrate with Web3 wallets, exchanges, and traditional SaaS services (e.g., WeChat Work, Slack), enabling cross-scenario interoperability of digital human identities.
1.1.5 Incentive & Governance Layer: Building a Value Closed Loop
Smart Contracts: Implement token incentives for training contributions, NFT-based rights confirmation for digital human assets, and support rights circulation such as NFT staking and trading.
Decentralized Governance: Plan to introduce a DAO mechanism, allowing core NFT holders to participate in community governance of API standards, copyright norms, and incentive policies.
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