CSM

Converts a single 2D image into a complete 3D mesh using neural implicit surface reconstruction and multi-view synthesis. The system analyzes the input image, infers depth and geometry through learned priors about object structure, and generates a watertight mesh optimized for real-time rendering. This approach bypasses the need for multiple reference images or sparse point clouds, making it accessible for rapid asset creation workflows.

Generates 3D meshes directly from natural language text descriptions using a diffusion-based or transformer-based generative model conditioned on text embeddings. The system interprets semantic intent from prompts, synthesizes plausible 3D geometry that matches the description, and produces optimized output suitable for real-time engines. This enables asset creation without requiring reference images or 3D expertise.

Converts sparse 3D point clouds or depth scans (e.g., from LiDAR, structured light, or photogrammetry) into dense, watertight meshes using learned implicit surface completion. The system fills gaps in sparse input data by inferring missing geometry based on learned shape priors and local surface continuity constraints. This bridges the gap between raw scanning hardware output and production-ready 3D assets.

Automatically generates UV coordinates for 3D meshes using learned seam placement and parametrization optimization, eliminating manual UV unwrapping. The system analyzes mesh topology, identifies optimal seam locations to minimize distortion, and produces a packed UV layout suitable for texture mapping. This is performed as part of the asset generation pipeline, ensuring textures can be applied immediately without additional tools.

Automatically generates physically-based rendering (PBR) texture maps (albedo, normal, roughness, metallic, ambient occlusion) for 3D meshes using neural texture synthesis and learned material properties. The system infers appropriate material characteristics from the input image or text description, synthesizes textures that are spatially coherent and physically plausible, and bakes them onto the generated UV layout. This produces complete, renderable assets without manual texture authoring.

Automatically optimizes generated 3D assets for real-time rendering by reducing polygon count, simplifying topology, and exporting to engine-specific formats (FBX, GLTF, Unreal Engine, Unity). The system applies mesh decimation, LOD generation, and format conversion while preserving visual quality and ensuring compatibility with target game engines. This produces immediately-usable assets without requiring manual optimization or re-export workflows.

Provides a REST/GraphQL API for programmatic batch generation of 3D assets, enabling integration into automated pipelines and CI/CD workflows. The system accepts bulk requests with multiple input images, text prompts, or scan data, processes them asynchronously, and returns completed assets with status tracking and error handling. This enables studios to automate large-scale asset production without manual intervention.

Converts multiple 2D images of the same object (taken from different viewpoints) into a single 3D mesh using structure-from-motion and multi-view stereo principles combined with neural implicit surface reconstruction. The system aligns images, computes depth from multiple views, and synthesizes a complete 3D model that incorporates information from all input perspectives. This produces higher-quality and more accurate reconstructions than single-image methods.