How KAIFX Generates High‑Quality Virtual Data for AI Training

AI and Computer Graphics

With deep learning booming, massive, accurate data has become essential for training AI models, especially in tasks that involve interaction between humans and physical objects such as object detection, pose estimation, and autonomous driving. Acquiring large‑scale 3D data is costly because algorithms need not only object boundaries but also spatial position and orientation, which are hard to capture in the real world.

Generating synthetic training data using computer‑graphics techniques has emerged as a promising solution. By building 3D models and rendering them photorealistically, developers can produce perfectly labeled data at marginal cost, attracting strong industrial interest.

However, practical deployment faces four major challenges:

The real world is extremely diverse, while virtual scenes are simplified; reducing the domain gap is critical.

High‑fidelity rendering remains computationally expensive; balancing realism and cost is difficult.

Labeling schemes for synthetic data can differ from manual annotations, leading to bias.

Managing millions of generated samples and using them to debug AI training pipelines is a substantial engineering effort.

KAIFX Platform Architecture

Module Overview

KAIFX consists of six major modules:

A、Procedural Virtual Data Generation Module: Combines CG, GAN and other generative techniques to create 3D scenes and render high‑quality synthetic data tailored to specific AI training needs.

B、Component‑Based Programmable Rendering Pipeline: Allows flexible configuration of rendering nodes via scripts, supporting pipelines such as photorealistic, non‑photorealistic, and label rendering.

C、Virtual Data Output Management Module: Enables scripted control of data distribution, producing samples with varied dimensions and statistical properties to improve training effectiveness.

D、Data Domain Transfer Module: Applies image‑level and AI algorithms to align the feature distribution of synthetic data with real data, narrowing the domain gap.

E、Data Quantitative Analysis Module: Uses measurement algorithms to compare synthetic and real data features, iteratively refining the generation process.

F、Virtual Data Database: Stores tens of millions of generated samples, providing efficient retrieval for AI training workflows.

KAIFX in Practice

1. Service for AR Scene Recognition

For AR applications, buildings and indoor spaces are key visual anchors. KAIFX creates an endless, non‑repeating virtual city containing buildings, streets, trees, traffic signs, vehicles, pedestrians, and dynamic sky conditions (sunny, cloudy, night). Generative networks also supply modeling, material, and texture parameters to enrich diversity.

Each virtual object receives detailed, accurate labels mirroring manual annotation, facilitating neural‑network training.

High‑fidelity rendering pipelines generate nearly one million labeled samples, after which domain‑transfer techniques reduce visual discrepancies between synthetic and real data.

The analysis shows a ~20% improvement in AR network performance compared with using only real data, demonstrating the efficiency of synthetic‑real hybrid training.

2. Service for 3D Face Reconstruction

Face detection and effect generation are core to Kuaishou’s products. Existing public face datasets lack the diversity needed for advanced tasks such as geometry, material, expression, lighting estimation, and style transfer.

KAIFX’s dedicated facial generation project creates synthetic data along three axes: diverse geometry modeling, diverse material & lighting modeling, and diverse expression & dynamic modeling.

Geometric modeling combines topological isomorphism with facial feature intervals to generate varied facial parameters. On top of geometry, generative models produce high‑precision texture maps (diffuse, specular, normal, subsurface scattering) for realistic facial rendering.

Parameterized control enables dynamic expression synthesis, extending static single‑image data to multi‑frame sequences.

These synthetic faces are then managed, domain‑transferred, and stored using the same KAIFX modules, providing abundant training data for facial‑related neural networks.

Future Goals

KAIFX will continue to refine its development workflow and synthetic data generation methods, aiming to provide ever‑greater assistance for AI model training across a broader range of applications.