Houdini is a 3D computer graphics software，Developed by Side Effects Software Inc. (SESI), a Canadian company founded by Kim Davidson and Greg Hermanovic in 1987. Houdini is a redevelopment based on Prisms, which can run on operating systems such as Linux, Windows, Mac OS, etc. It is designed entirely based on node mode and has significant differences in structure, operation mode, and other 3D software compared to other software. Houdini's built-in renderer is Mantra, based on the Reyes rendering architecture, which can also quickly render motion blur, depth of field, and displacement effects.

| What is synthetic data?

Synthetic data is artificially generated through algorithms, rather than collected from the real world, so there are no issues of copyright infringement or sensitive information leakage. It can be generated through simulation or random processes to replicate the structure and variability of real data, covering various formats such as text, numerical values, images, audio, and 3D geometry. Due to its completely controllable quality and content, synthetic data is highly suitable for safe and ethical training of artificial intelligence models, enabling development based on completely original datasets. Synthetic data also has a high degree of information disclosure control capability, which helps to reduce privacy and legal risks. This makes it particularly valuable in environments that require strict data governance, allowing for flexible use and sharing between public and private domains without sacrificing data confidentiality.

| Using synthetic data for machine learning and artificial intelligence training

Synthetic data plays a crucial role in machine learning and artificial intelligence, as it overcomes many limitations of real-world data through artificially generated datasets. Through algorithms and random processes, synthetic data can create large-scale, diverse, and balanced datasets, enabling more efficient model training, especially suitable for scenarios where real data is scarce, sensitive, or expensive to obtain. Synthetic data can also precisely control the quality and variability of data, enabling developers to cover rare cases and reduce bias. More importantly, as synthetic data does not contain genuine personal information, it greatly reduces privacy risks and helps comply with data protection regulations. This makes synthetic data an indispensable tool for building robust, fair, and privacy conscious artificial intelligence systems, suitable for various application scenarios.

Synthetic data will really scale AI: Announcing our Series A in Parallel Domain

| Creating synthetic data using Houdini

Houdini has a fully programmed, node based workflow that provides a powerful and flexible solution for large-scale generation of synthetic data, particularly suitable for meeting the complex needs of machine learning and artificial intelligence. By building an intelligent and customizable system in Houdini, users can quickly generate highly diverse 3D environments, randomized object interactions, and fine simulation effects such as smoke, fluids, crowds, etc., and can precisely control parameters and random variations. This method supports scalable production of a large variety of datasets, accurately reflecting the complexity and variability of the real world, and is key to training robust AI models.



In addition, Houdini supports exporting metadata and tags, and seamlessly integrates them through scripts and mainstream data formats, thereby improving automation efficiency and simplifying integration with existing data processing pipelines. Whether in computer vision, robotics, or simulation driven AI applications, Houdini can help users tailor high-quality synthetic datasets based on specific machine learning needs.



Its programmatic features enable users to quickly iterate and automate the generation of diverse scenarios and environments, greatly accelerating the development process of AI. By finely controlling the variability and annotation of data in large-scale generation, Houdini helps improve the accuracy, robustness, and generalization ability of the model, while reducing reliance on scarce or sensitive real data.

Scaling Simulation Workflows with Houdini, OpenUSD and NVIDIA Omniverse 

|  Annotate synthetic data for training

SideFX collaborates with Endava to convert synthetic data for AI and ML

Example Houdini file ML Gauge Synthetic Data

| Casestudy

Tesla - Simulation Platform Accelerates Tesla Autonomous Driving

Applied Intuition

Program generation based on application intuition and Toyota - create simulation for autonomous vehicle Create programmed terrain using Houdini

Amazon Robotics

Amazon Robotics combines the powerful capabilities of NVIDIA Omniverse and Adobe Substance 3D to simulate warehouse operations

Amazon Robotics utilizes Houdini to programmatically generate diverse 3D assets, such as virtual packages, for AI model training in warehouse operations. By integrating Houdini's programmatic dependency graph (PDG) with Adobe Substance 3D and NVIDIA Omniverse, they developed scalable workflows that generate realistic and diverse synthetic data, thereby improving the efficiency and accuracy of robot perception systems.

Starting from Houdini version 20.5, we are able to create programmatic textures similar to Substance in Houdini.

Synthesis AI

Automated human body synthesis: from real text to digital humans

Synthesis AI combines generative artificial intelligence with traditional programmatic workflows using Houdini to build a flexible AWS cloud based platform that enables unlimited automation of asset and synthetic data production.

Bifrost

Bifrost uses Houdini to create realistic environments and diverse scenes, and renders them in the Unreal engine for computer vision training.

Obstacle changes

Different scene changes

| User Stories

Using Houdini, Python, and Tensorflow for image recognition and synthetic data generation
Based on the LEGO manual, train a machine learning model using Houdini to generate LEGO models