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This approach bypasses the need for multiple reference images or sparse point clouds, making it accessible for rapid asset creation workflows.","intents":["I want to convert a product photo into a 3D model for my game without manual modeling","I need to quickly prototype 3D assets from reference images for visualization","I want to automate the conversion of 2D concept art into game-ready geometry"],"best_for":["game developers prototyping assets quickly","3D content creators automating tedious modeling tasks","product visualization teams converting marketing images to interactive 3D"],"limitations":["Single-image inference may struggle with highly occluded or transparent objects","Complex articulated structures (e.g., human poses) may require post-processing refinement","Output quality depends heavily on input image clarity and lighting conditions","Cannot infer internal geometry or hollow structures from external views alone"],"requires":["Input image in common formats (JPG, PNG, WebP)","API key for CSM service","Network connectivity for cloud processing"],"input_types":["image (single 2D photograph or rendered view)"],"output_types":["3D mesh (OBJ, FBX, GLTF formats)","vertex positions and normals","topology-optimized geometry"],"categories":["image-visual","3d-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"csm__cap_1","uri":"capability://image.visual.text.prompt.to.3d.asset.generation","name":"text-prompt-to-3d-asset-generation","description":"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.","intents":["I want to generate a fantasy sword with specific visual characteristics just by describing it","I need to create multiple variations of a game asset by tweaking text descriptions","I want to rapidly prototype environmental objects without reference images"],"best_for":["game designers iterating on asset concepts","indie developers without 3D art teams","rapid prototyping and pre-visualization workflows"],"limitations":["Text-to-3D generation is less deterministic than image-to-3D; results may vary significantly between runs","Complex or highly specific descriptions may produce ambiguous or unrealistic geometry","Fine-grained control over specific geometric details is limited compared to manual modeling","Prompt engineering required for consistent, high-quality outputs"],"requires":["Text prompt in English (other languages may have reduced quality)","API key for CSM service","Network connectivity for cloud processing"],"input_types":["text (natural language description of desired 3D asset)"],"output_types":["3D mesh (OBJ, FBX, GLTF formats)","vertex positions and normals","topology-optimized geometry"],"categories":["image-visual","text-generation-language","3d-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"csm__cap_2","uri":"capability://image.visual.sparse.scan.to.dense.mesh.reconstruction","name":"sparse-scan-to-dense-mesh-reconstruction","description":"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.","intents":["I have a LiDAR scan of a room and need a complete mesh for game level design","I want to convert a sparse point cloud from photogrammetry into a solid, renderable mesh","I need to fill holes and smooth artifacts in a 3D scan for real-time use"],"best_for":["architectural visualization teams processing building scans","game developers creating levels from real-world scans","robotics teams converting sensor data to 3D assets"],"limitations":["Dense reconstruction assumes reasonable surface continuity; highly fragmented scans may produce artifacts","Inferred geometry in sparse regions may not match actual unmeasured surfaces","Very large point clouds (>10M points) may require downsampling before processing","Semantic understanding of scan content is limited; cannot distinguish between intentional geometry and noise"],"requires":["Sparse 3D data in formats like PLY, XYZ, or PCD","API key for CSM service","Network connectivity for cloud processing"],"input_types":["point cloud (sparse 3D coordinates, optionally with normals or colors)","depth maps (2D depth images from structured light or ToF sensors)"],"output_types":["3D mesh (OBJ, FBX, GLTF formats)","dense vertex positions and normals","watertight topology"],"categories":["image-visual","data-processing-analysis","3d-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"csm__cap_3","uri":"capability://image.visual.automatic.uv.mapping.and.unwrapping","name":"automatic-uv-mapping-and-unwrapping","description":"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.","intents":["I want generated 3D assets to be immediately texture-ready without manual UV unwrapping","I need to batch-process multiple meshes with consistent UV layouts for texture atlasing","I want to avoid the tedious manual UV unwrapping step in my asset pipeline"],"best_for":["game developers automating asset production pipelines","studios with large asset volumes and limited UV artists","rapid prototyping workflows where UV quality is secondary to speed"],"limitations":["Automatic seam placement may not match artistic intent for stylized assets","Complex topology with many hard edges may result in suboptimal seam placement","UV distortion is minimized but not guaranteed to be zero; high-fidelity normal maps may show artifacts","Cannot handle user-specified seam preferences or artistic UV layouts"],"requires":["3D mesh output from CSM generation pipeline","No additional tools required; integrated into generation workflow"],"input_types":["3D mesh (generated or imported)"],"output_types":["3D mesh with UV coordinates (U, V per vertex)","UV layout metadata (seam positions, island boundaries)"],"categories":["image-visual","data-processing-analysis","3d-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"csm__cap_4","uri":"capability://image.visual.pbr.texture.generation.and.baking","name":"pbr-texture-generation-and-baking","description":"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.","intents":["I want generated 3D assets to come with complete PBR textures ready for game engines","I need to automatically create material variations for the same base geometry","I want to avoid hiring texture artists for rapid prototyping workflows"],"best_for":["game studios automating end-to-end asset production","indie developers with limited art resources","rapid prototyping and visualization workflows"],"limitations":["Generated textures may lack fine artistic detail or hand-crafted quality","Complex material properties (e.g., anisotropic surfaces, subsurface scattering) are not fully supported","Texture resolution is fixed (typically 2K or 4K); custom resolutions may not be available","Generated textures may show tiling artifacts or lack variation across large surfaces","Cannot incorporate custom material specifications or brand-specific color palettes"],"requires":["3D mesh with UV coordinates (generated by CSM or imported)","API key for CSM service","Target game engine compatibility (Unity, Unreal, custom)"],"input_types":["3D mesh with UV layout","reference image or text description (optional, for material guidance)"],"output_types":["PBR texture maps (albedo, normal, roughness, metallic, AO)","texture format (PNG, TGA, or engine-specific formats)","texture resolution metadata"],"categories":["image-visual","data-processing-analysis","3d-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"csm__cap_5","uri":"capability://image.visual.real.time.engine.optimization.and.export","name":"real-time-engine-optimization-and-export","description":"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.","intents":["I want to generate 3D assets that work immediately in my game engine without optimization","I need to create multiple LOD versions of assets for performance optimization","I want to export assets in the exact format my game engine requires"],"best_for":["game developers building performance-critical applications","studios with large asset volumes requiring consistent optimization","mobile game developers with strict polygon budgets"],"limitations":["Automatic LOD generation may not match hand-crafted LOD quality for complex assets","Polygon reduction is lossy; very aggressive optimization may introduce visible artifacts","Engine-specific features (e.g., Unreal's Nanite) may require additional setup beyond export","Custom optimization profiles or per-asset tuning is not available; one-size-fits-all approach"],"requires":["Target game engine specified (Unity, Unreal Engine, custom)","Engine version compatibility information","API key for CSM service"],"input_types":["3D mesh with textures (generated or imported)"],"output_types":["optimized 3D mesh (FBX, GLTF, or engine-native format)","LOD variants (multiple polygon count versions)","engine-specific material definitions","metadata (polygon count, texture resolution, performance estimates)"],"categories":["image-visual","data-processing-analysis","3d-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"csm__cap_6","uri":"capability://tool.use.integration.batch.asset.generation.with.api","name":"batch-asset-generation-with-api","description":"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.","intents":["I want to integrate 3D asset generation into my automated content pipeline","I need to generate hundreds of assets programmatically from a database of images","I want to trigger asset generation from my game engine or content management system"],"best_for":["game studios with large asset production volumes","procedural game developers generating content at scale","content management systems integrating 3D asset generation"],"limitations":["API rate limits may restrict throughput for very large batch jobs","Asynchronous processing introduces latency; real-time generation is not supported","Batch processing may have longer queue times during peak usage","Error handling and retry logic must be implemented by the client"],"requires":["API key for CSM service","HTTP client library (curl, requests, axios, etc.)","Webhook endpoint for receiving completion notifications (optional)","Network connectivity and ability to handle asynchronous responses"],"input_types":["JSON request body with image URLs, text prompts, or scan data","multipart form data for file uploads"],"output_types":["JSON response with job ID, status, and asset URLs","3D mesh files (downloadable via returned URLs)","webhook notifications with completion status"],"categories":["tool-use-integration","automation-workflow","3d-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"csm__cap_7","uri":"capability://image.visual.multi.view.image.to.3d.reconstruction","name":"multi-view-image-to-3d-reconstruction","description":"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.","intents":["I have multiple photos of a sculpture and want to create a 3D model from them","I want to reconstruct a physical object from a series of reference images for game development","I need to create accurate 3D models from photogrammetry-style image sets"],"best_for":["game developers with access to multiple reference images","product visualization teams with photography setups","museums and cultural heritage projects digitizing artifacts"],"limitations":["Requires multiple images from different viewpoints; single-image fallback may be lower quality","Image alignment may fail if viewpoints are too similar or too different","Reflective or transparent surfaces may cause reconstruction artifacts","Requires sufficient overlap between images for feature matching"],"requires":["Multiple images (typically 4-20) of the same object from different angles","Images in common formats (JPG, PNG, WebP)","API key for CSM service"],"input_types":["image set (multiple 2D photographs from different viewpoints)"],"output_types":["3D mesh (OBJ, FBX, GLTF formats)","vertex positions and normals","camera pose estimates (optional)"],"categories":["image-visual","data-processing-analysis","3d-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"csm__headline","uri":"capability://image.visual.ai.powered.3d.asset.generation.from.images.and.text","name":"ai-powered 3d asset generation from images and text","description":"Common Sense Machines specializes in creating game-ready and world-ready 3D assets using AI, transforming single images, text, or sparse scans into optimized models with automatic UV mapping and PBR textures for real-time rendering.","intents":["best AI 3D asset generator","3D generation for game development","AI tools for creating 3D models from images","best software for generating 3D assets from text","AI solutions for real-time 3D rendering"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Input image in common formats (JPG, PNG, WebP)","API key for CSM service","Network connectivity for cloud processing","Text prompt in English (other languages may have reduced quality)","Sparse 3D data in formats like PLY, XYZ, or PCD","3D mesh output from CSM generation pipeline","No additional tools required; integrated into generation workflow","3D mesh with UV coordinates (generated by CSM or imported)","Target game engine compatibility (Unity, Unreal, custom)","Target game engine specified (Unity, Unreal Engine, custom)"],"failure_modes":["Single-image inference may struggle with highly occluded or transparent objects","Complex articulated structures (e.g., human poses) may require post-processing refinement","Output quality depends heavily on input image clarity and lighting conditions","Cannot infer internal geometry or hollow structures from external views alone","Text-to-3D generation is less deterministic than image-to-3D; results may vary significantly between runs","Complex or highly specific descriptions may produce ambiguous or unrealistic geometry","Fine-grained control over specific geometric details is limited compared to manual modeling","Prompt engineering required for consistent, high-quality outputs","Dense reconstruction assumes reasonable surface continuity; highly fragmented scans may produce artifacts","Inferred geometry in sparse regions may not match actual unmeasured surfaces","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.548Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=csm","compare_url":"https://unfragile.ai/compare?artifact=csm"}},"signature":"h+5Qd1TojNJ0cPegc7vNxBBa9eFXq0RVBBjANWir/9eAT5buhpMt1NZCyBiJmV+lr9WRtMxBj3F7htl1LjxkAw==","signedAt":"2026-06-22T05:16:59.516Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/csm","artifact":"https://unfragile.ai/csm","verify":"https://unfragile.ai/api/v1/verify?slug=csm","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}