Enterprise-grade automated 3D avatar generation for games, XR, and the spatial web.
Didimo is a leading-edge digital human platform that leverages advanced computer vision and neural networks to transform 2D imagery into high-fidelity, fully rigged 3D avatars in under 90 seconds. Positioned as a mission-critical infrastructure for the 2026 spatial web, Didimo's architecture focuses on interoperability and scalability. Unlike generic avatar creators, Didimo provides a 'Universal Rig' compatible with ARKit blendshapes and industry-standard animation pipelines, including Mixamo and AWS Amazon Polly. Their proprietary 'Populate' tool allows developers to ingest massive datasets of user photos to generate diverse, unique NPC populations or user-representative avatars for MMOs and social platforms. Technically, the platform excels in automated retopology and Level of Detail (LOD) management, ensuring that generated assets are optimized for both mobile AR and high-end PC/Console environments. As of 2026, Didimo has integrated deeply with real-time facial performance capture systems, making it a primary choice for developers requiring authentic, emotionally expressive digital doubles without the high costs of traditional photogrammetry booths.
A standardized bone structure and weight painting system compatible with major animation libraries.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Includes 52+ standard Apple ARKit facial blendshapes for real-time performance capture.
Generates 4K PBR (Physically Based Rendering) texture maps (albedo, normal, specular, roughness) from a single photo.
Modular asset swapping system that allows for procedural variation of hair styles and outfits on the same base mesh.
Automatically generates multiple simplified versions of the mesh for distant rendering performance.
Highly scalable REST API capable of processing thousands of unique faces simultaneously.
Pre-configured facial poses for basic human emotions (Joy, Anger, Surprise) accessible via API parameters.
Players want to see themselves in-game but traditional sliders are difficult to use and time-consuming.
Registry Updated:2/7/2026
User begins playing with their likeness.
Generic avatars reduce immersion and emotional connection in sensitive training scenarios.
2D overlays don't account for head shape and depth, leading to inaccurate sizing.