{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-tencent--hunyuan3d-2","slug":"tencent--hunyuan3d-2","name":"Hunyuan3D-2","type":"webapp","url":"https://huggingface.co/spaces/tencent/Hunyuan3D-2","page_url":"https://unfragile.ai/tencent--hunyuan3d-2","categories":["automation"],"tags":["gradio","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-tencent--hunyuan3d-2__cap_0","uri":"capability://image.visual.text.to.3d.model.generation.from.image.and.text.prompts","name":"text-to-3d model generation from image and text prompts","description":"Generates 3D models from combined image and text inputs using a diffusion-based architecture that processes visual and linguistic features through a unified latent space. The system leverages Hunyuan's multi-modal encoder to align image semantics with text descriptions, then applies iterative denoising in 3D space to produce textured mesh outputs. This approach enables semantic-aware 3D generation where both image composition and text details influence the final geometry and appearance.","intents":["Generate 3D assets from product photos and detailed descriptions for e-commerce or game development","Create 3D models from concept art sketches combined with narrative prompts for creative workflows","Rapidly prototype 3D objects from reference images without manual modeling","Convert 2D visual references into production-ready 3D geometry with texture"],"best_for":["3D content creators and game developers seeking rapid asset generation","Product designers prototyping 3D models from 2D references","Teams automating 3D asset pipelines for e-commerce or metaverse applications","Researchers exploring multi-modal 3D generation architectures"],"limitations":["Output quality heavily dependent on input image clarity and text prompt specificity; ambiguous inputs produce inconsistent geometry","Generated models may require post-processing in 3D software for production use; topology and UV mapping are not optimized for animation","Inference latency typically 30-120 seconds per model depending on resolution and complexity parameters","Limited control over specific geometric features; generation is probabilistic and may not match exact specifications","Memory requirements scale with output resolution; high-resolution generation (>2K) may timeout on resource-constrained environments"],"requires":["Modern GPU with CUDA support (NVIDIA RTX 3060+ or equivalent) for reasonable inference speed","Minimum 8GB VRAM; 16GB+ recommended for batch processing","Internet connection for HuggingFace Spaces access or local deployment with model weights (~10-15GB)","Image input: JPEG/PNG format, recommended 512x512 to 1024x1024 resolution","Text input: UTF-8 encoded prompts, 10-200 tokens optimal length"],"input_types":["image (JPEG, PNG, WebP)","text (natural language prompt)"],"output_types":["3D mesh (GLB, OBJ format)","textured geometry with vertex colors or texture maps","preview renders (PNG)"],"categories":["image-visual","3d-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-tencent--hunyuan3d-2__cap_1","uri":"capability://image.visual.interactive.3d.model.preview.and.manipulation.in.browser","name":"interactive 3d model preview and manipulation in browser","description":"Provides real-time WebGL-based 3D visualization of generated models within the Gradio interface, enabling users to rotate, zoom, and inspect geometry without external software. The implementation uses Three.js or similar WebGL renderer integrated into the Gradio output component, with automatic lighting setup and material assignment to showcase generated textures and geometry details.","intents":["Inspect generated 3D models immediately after generation without downloading or opening external software","Verify model quality and geometric accuracy before export or further processing","Share 3D model previews with stakeholders through shareable Spaces links","Iterate on prompts by quickly comparing multiple generated variants"],"best_for":["Designers and artists iterating on 3D generation prompts in real-time","Teams reviewing generated assets before production integration","Non-technical stakeholders evaluating 3D output quality without 3D software knowledge"],"limitations":["Browser-based rendering limited to ~1M polygons before performance degradation; high-poly models may require decimation","No advanced material editing or PBR workflow support; preview uses simplified shading","Mobile browser support inconsistent; optimal experience on desktop with WebGL 2.0 support","No collaborative annotation or measurement tools; inspection is visual only"],"requires":["Modern web browser with WebGL 2.0 support (Chrome 56+, Firefox 51+, Safari 15+)","JavaScript enabled","Stable internet connection for real-time rendering"],"input_types":["3D mesh (GLB, OBJ)"],"output_types":["interactive 3D viewport (WebGL canvas)","downloadable mesh file (GLB/OBJ)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-tencent--hunyuan3d-2__cap_2","uri":"capability://automation.workflow.batch.3d.model.generation.with.parameter.sweep","name":"batch 3d model generation with parameter sweep","description":"Enables sequential or parallel generation of multiple 3D models by varying text prompts, image inputs, or generation parameters (e.g., diffusion steps, guidance scale) through Gradio's batch processing interface. The backend queues requests and manages GPU allocation across multiple generation jobs, with results aggregated and downloadable as a batch archive.","intents":["Generate multiple 3D asset variants from a single reference image with different style or detail prompts","Explore parameter sensitivity by generating models with varying diffusion step counts or guidance scales","Create 3D asset libraries by batch-processing collections of product photos","Benchmark generation quality across different prompt formulations"],"best_for":["Content studios producing large 3D asset libraries","Researchers conducting ablation studies on generation parameters","Teams optimizing prompt templates for consistent quality"],"limitations":["Batch processing queued sequentially on shared HuggingFace Spaces GPU; total time scales linearly with batch size","No priority queuing or resource reservation; batch jobs may be delayed during peak usage","Results not persisted across sessions; batch outputs must be downloaded immediately or lost","Limited to ~50-100 models per batch before timeout on free tier; larger batches require dedicated deployment"],"requires":["HuggingFace Spaces account for extended session duration","CSV or JSON file with prompt/parameter specifications","Patience for sequential processing (30-120 seconds per model × batch size)"],"input_types":["text (CSV/JSON with prompts and parameters)","image (batch of reference images)"],"output_types":["ZIP archive containing GLB models and preview images","CSV metadata file with generation parameters and timestamps"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-tencent--hunyuan3d-2__cap_3","uri":"capability://data.processing.analysis.model.export.and.format.conversion","name":"model export and format conversion","description":"Exports generated 3D models in multiple formats (GLB, OBJ, USDZ) with automatic topology optimization and material baking. The system converts the internal mesh representation to target formats, optionally applies decimation for file size reduction, and embeds textures or generates texture atlases depending on the output format requirements.","intents":["Export 3D models for use in game engines (Unity, Unreal) requiring specific formats and optimization levels","Convert models to USDZ for AR applications on iOS/web platforms","Generate optimized models for real-time rendering with reduced polygon counts","Prepare models for 3D printing by exporting to formats compatible with slicing software"],"best_for":["Game developers integrating generated assets into production pipelines","AR/VR developers targeting specific platform requirements","3D printing services requiring manifold geometry and specific file formats"],"limitations":["Automatic decimation may introduce visual artifacts on high-detail models; manual refinement often necessary","Texture baking resolution fixed at 1K or 2K; custom resolution not exposed in UI","USDZ export may lose material complexity; PBR workflows not fully preserved","No support for rigging, skeletal animation, or blend shapes; exported models are static geometry only"],"requires":["Generated 3D model in internal representation","Target format selection (GLB, OBJ, USDZ)","Optional: target polygon count for decimation"],"input_types":["3D mesh (internal representation)"],"output_types":["GLB (glTF binary with embedded textures)","OBJ (Wavefront with MTL material file)","USDZ (USD Zip archive for AR)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-tencent--hunyuan3d-2__cap_4","uri":"capability://text.generation.language.prompt.engineering.and.semantic.search.for.generation.parameters","name":"prompt engineering and semantic search for generation parameters","description":"Provides UI guidance and example prompts to help users formulate effective text inputs for 3D generation. The system may include a searchable prompt library or suggestion engine that recommends prompt templates based on user intent (e.g., 'photorealistic product', 'stylized character', 'architectural model'). Integrates semantic understanding to map natural language descriptions to effective generation parameters.","intents":["Learn effective prompt formulations for consistent, high-quality 3D generation","Discover prompt templates for common use cases without trial-and-error","Understand how text descriptions influence 3D geometry and appearance","Optimize prompts for specific aesthetic or functional requirements"],"best_for":["Non-technical users new to 3D generation seeking guidance","Content creators optimizing prompt templates for brand consistency","Teams establishing prompt best practices and style guides"],"limitations":["Prompt suggestions are heuristic-based; no guarantee of optimal results for novel use cases","Library of example prompts may be limited or domain-specific; coverage of niche use cases incomplete","No A/B testing framework to systematically evaluate prompt variations","Semantic understanding limited to training data; novel or specialized terminology may not be recognized"],"requires":["Access to prompt library or suggestion engine (may require internet connection)","Basic understanding of descriptive language for 3D concepts"],"input_types":["text (user intent or partial prompt)"],"output_types":["text (suggested prompts or templates)","structured metadata (prompt category, recommended parameters)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-tencent--hunyuan3d-2__cap_5","uri":"capability://automation.workflow.gpu.accelerated.diffusion.inference.with.adaptive.scheduling","name":"gpu-accelerated diffusion inference with adaptive scheduling","description":"Executes the 3D diffusion model on GPU hardware with optimized inference scheduling, including dynamic batch sizing, mixed-precision computation (FP16/BF16), and adaptive step scheduling to balance quality and latency. The system monitors GPU memory and adjusts computation strategy (e.g., gradient checkpointing, activation quantization) to fit within available resources while maintaining generation quality.","intents":["Generate 3D models with sub-2-minute latency on consumer-grade GPUs","Maximize GPU utilization for cost-effective inference on shared hardware","Support variable-resolution generation without out-of-memory errors","Enable real-time or near-real-time iteration on generation parameters"],"best_for":["Inference service operators optimizing cost and throughput on shared GPU clusters","Researchers benchmarking diffusion model efficiency","Teams deploying 3D generation at scale with resource constraints"],"limitations":["Mixed-precision computation may introduce subtle quality degradation on edge cases; full FP32 fallback slower","Adaptive scheduling adds ~5-10% latency overhead for memory monitoring and adjustment logic","Batch size optimization requires profiling; suboptimal for highly variable input sizes","GPU memory fragmentation over time may cause occasional OOM errors; periodic restart recommended"],"requires":["NVIDIA GPU with CUDA Compute Capability 7.0+ (RTX 2060 or newer)","CUDA 11.8+ and cuDNN 8.6+","PyTorch 2.0+ with CUDA support","Minimum 8GB VRAM; 16GB+ for optimal throughput"],"input_types":["model weights (PyTorch checkpoint)","generation parameters (resolution, steps, guidance scale)"],"output_types":["3D mesh (latent representation)","performance metrics (latency, memory usage)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-tencent--hunyuan3d-2__cap_6","uri":"capability://data.processing.analysis.multi.view.3d.model.consistency.validation","name":"multi-view 3d model consistency validation","description":"Validates geometric consistency and visual quality of generated 3D models by rendering multiple views and comparing against expected properties (e.g., symmetry, surface smoothness, texture coherence). The system may use auxiliary networks or heuristics to detect artifacts like self-intersections, holes, or unrealistic geometry, providing feedback on generation quality without manual inspection.","intents":["Automatically filter low-quality generations before export or further processing","Detect geometric artifacts (self-intersections, holes, non-manifold geometry) that require manual repair","Validate that generated models meet quality thresholds for production use","Provide quantitative quality metrics for generation parameter tuning"],"best_for":["Automated asset pipelines requiring quality gates before downstream processing","Teams establishing quality standards for generated 3D content","Researchers analyzing generation failure modes and artifact types"],"limitations":["Validation heuristics may produce false positives/negatives; not a substitute for manual review on critical assets","Consistency checks computationally expensive; add 10-30 seconds per model to total pipeline time","Validation metrics may not align with human perception of quality; subjective aesthetic judgments not captured","Limited to geometric and topological validation; semantic correctness (e.g., 'does this look like a chair?') not assessed"],"requires":["Generated 3D model in mesh format","Optional: reference geometry or quality thresholds for comparison"],"input_types":["3D mesh (GLB, OBJ)"],"output_types":["quality score (0-100)","artifact report (list of detected issues)","multi-view renders for visual inspection"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-tencent--hunyuan3d-2__cap_7","uri":"capability://memory.knowledge.session.based.generation.history.and.comparison","name":"session-based generation history and comparison","description":"Maintains a browsable history of all 3D models generated within a user session, with metadata (prompts, parameters, timestamps) and side-by-side comparison tools. Users can review previous generations, compare variants, and re-generate with modified parameters without losing context. History is stored in browser local storage or server-side session state depending on deployment.","intents":["Review and compare multiple generation attempts to identify best results","Iterate on prompts by modifying previous successful generations","Document generation process and parameter choices for reproducibility","Share generation history with collaborators for feedback"],"best_for":["Designers iterating on 3D generation prompts within a single session","Teams collaborating on asset generation with shared history","Researchers documenting generation experiments and parameter sensitivity"],"limitations":["History limited to current session; no persistence across browser sessions or devices without explicit export","Comparison tools limited to visual inspection; no quantitative metrics for objective quality assessment","Large histories (100+ models) may degrade UI responsiveness; pagination or lazy loading required","No version control or branching; linear history only"],"requires":["Browser local storage or server-side session management","Sufficient storage for model metadata and preview images (~1-5MB per model)"],"input_types":["generation metadata (prompts, parameters, timestamps)"],"output_types":["browsable history UI","comparison view (side-by-side renders)","exportable session report (JSON or CSV)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Modern GPU with CUDA support (NVIDIA RTX 3060+ or equivalent) for reasonable inference speed","Minimum 8GB VRAM; 16GB+ recommended for batch processing","Internet connection for HuggingFace Spaces access or local deployment with model weights (~10-15GB)","Image input: JPEG/PNG format, recommended 512x512 to 1024x1024 resolution","Text input: UTF-8 encoded prompts, 10-200 tokens optimal length","Modern web browser with WebGL 2.0 support (Chrome 56+, Firefox 51+, Safari 15+)","JavaScript enabled","Stable internet connection for real-time rendering","HuggingFace Spaces account for extended session duration","CSV or JSON file with prompt/parameter specifications"],"failure_modes":["Output quality heavily dependent on input image clarity and text prompt specificity; ambiguous inputs produce inconsistent geometry","Generated models may require post-processing in 3D software for production use; topology and UV mapping are not optimized for animation","Inference latency typically 30-120 seconds per model depending on resolution and complexity parameters","Limited control over specific geometric features; generation is probabilistic and may not match exact specifications","Memory requirements scale with output resolution; high-resolution generation (>2K) may timeout on resource-constrained environments","Browser-based rendering limited to ~1M polygons before performance degradation; high-poly models may require decimation","No advanced material editing or PBR workflow support; preview uses simplified shading","Mobile browser support inconsistent; optimal experience on desktop with WebGL 2.0 support","No collaborative annotation or measurement tools; inspection is visual only","Batch processing queued sequentially on shared HuggingFace Spaces GPU; total time scales linearly with batch size","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.36,"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":"2026-05-24T12:16:23.325Z","last_scraped_at":"2026-05-03T14:22:48.012Z","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=tencent--hunyuan3d-2","compare_url":"https://unfragile.ai/compare?artifact=tencent--hunyuan3d-2"}},"signature":"OhnMyPv/AhdcB4OzF00Z6ItXoalcA4HCdGccE13CgQnyuECln/hzvpgtaKq5hF/It5+KyC2YIsRJylksGfB6Dw==","signedAt":"2026-06-23T03:33:21.040Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tencent--hunyuan3d-2","artifact":"https://unfragile.ai/tencent--hunyuan3d-2","verify":"https://unfragile.ai/api/v1/verify?slug=tencent--hunyuan3d-2","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"}}