{"adoption": {"forks": 132, "observed_at": "2026-08-28T04:05:11.081798+00:00", "stars": 1612}, "canonical_url": "https://ross.abutalabs.com/products/giga-world-0", "card": {"archived": false, "artifact_type": "framework", "description": "GigaWorld-0: World Models as Data Engine to Empower Embodied AI", "domain": ["machine-learning", "robotics", "simulation", "artificial-intelligence"], "enriched": true, "function": ["machine-learning", "deep-learning", "video-processing", "simulation", "data-generation", "computer-vision"], "health_score": 53, "homepage": null, "language": "Python", "license": "Apache-2.0", "license_family": "permissive", "maturity": "active", "member_repos": ["open-gigaai/giga-world-0"], "name": "open-gigaai/giga-world-0", "platform": ["python"], "pushed_at": "2025-12-03T08:56:40+00:00", "repo": "open-gigaai/giga-world-0", "stars": 1612, "tags": ["world-models", "embodied-ai", "vla", "video-generation", "3d-gaussian-splatting", "data-engine", "synthetic-data", "research", "video", "linux", "gpu"], "topics": [], "urls": [], "use_cases": ["generate synthetic training data for robot learning", "build a world model for embodied AI", "generate controllable embodied video sequences", "create VLA training datasets with video generation", "reconstruct 3D scenes with gaussian splatting for robotics", "simulate physically consistent robot interactions for data augmentation"], "what_it_is": "GigaWorld-0 is a unified world model framework that acts as a data engine for Vision-Language-Action (VLA) learning in embodied AI. It combines large-scale controllable video generation with 3D generative modeling, Gaussian Splatting reconstruction, and physically differentiable system identification to produce realistic embodied training data.", "when_to_avoid": ["you need a lightweight general-purpose video generation tool without embodied AI focus", "you lack GPU resources, as the framework targets large-scale generative model training and inference", "you need a production robotics stack rather than a research data-generation framework"], "when_to_choose": ["you need scalable synthetic data generation for vision-language-action model training", "you want controllable, texture-rich embodied video sequences with action semantics", "you need geometrically and physically consistent 3D embodied scenes", "you are researching world models as data engines for robotics"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/giga-world-0", "repo": "open-gigaai/giga-world-0", "role": "main", "score": 41}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T03:51:10.610553+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8d7ca365f7f7d0674c21aac7c3a7b02955ccca2427e8e40a0809fb1bb0914894", "fetched_at": "2026-08-28T04:05:11.081798+00:00", "kind": "readme", "missing": false, "url": "https://github.com/open-gigaai/giga-world-0"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T03:51:10.610553+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8d7ca365f7f7d0674c21aac7c3a7b02955ccca2427e8e40a0809fb1bb0914894", "fetched_at": "2026-08-28T04:05:11.081798+00:00", "kind": "readme", "missing": false, "url": "https://github.com/open-gigaai/giga-world-0"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T03:51:10.610553+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8d7ca365f7f7d0674c21aac7c3a7b02955ccca2427e8e40a0809fb1bb0914894", "fetched_at": "2026-08-28T04:05:11.081798+00:00", "kind": "readme", "missing": false, "url": "https://github.com/open-gigaai/giga-world-0"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T03:51:10.610553+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8d7ca365f7f7d0674c21aac7c3a7b02955ccca2427e8e40a0809fb1bb0914894", "fetched_at": "2026-08-28T04:05:11.081798+00:00", "kind": "readme", "missing": false, "url": "https://github.com/open-gigaai/giga-world-0"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T03:51:10.610553+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8d7ca365f7f7d0674c21aac7c3a7b02955ccca2427e8e40a0809fb1bb0914894", "fetched_at": "2026-08-28T04:05:11.081798+00:00", "kind": "readme", "missing": false, "url": "https://github.com/open-gigaai/giga-world-0"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T03:51:10.610553+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8d7ca365f7f7d0674c21aac7c3a7b02955ccca2427e8e40a0809fb1bb0914894", "fetched_at": "2026-08-28T04:05:11.081798+00:00", "kind": "readme", "missing": false, "url": "https://github.com/open-gigaai/giga-world-0"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:05:11.081798+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T03:51:10.610553+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8d7ca365f7f7d0674c21aac7c3a7b02955ccca2427e8e40a0809fb1bb0914894", "fetched_at": "2026-08-28T04:05:11.081798+00:00", "kind": "readme", "missing": false, "url": "https://github.com/open-gigaai/giga-world-0"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T03:51:10.610553+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8d7ca365f7f7d0674c21aac7c3a7b02955ccca2427e8e40a0809fb1bb0914894", "fetched_at": "2026-08-28T04:05:11.081798+00:00", "kind": "readme", "missing": false, "url": "https://github.com/open-gigaai/giga-world-0"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T03:51:10.610553+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8d7ca365f7f7d0674c21aac7c3a7b02955ccca2427e8e40a0809fb1bb0914894", "fetched_at": "2026-08-28T04:05:11.081798+00:00", "kind": "readme", "missing": false, "url": "https://github.com/open-gigaai/giga-world-0"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T03:51:10.610553+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8d7ca365f7f7d0674c21aac7c3a7b02955ccca2427e8e40a0809fb1bb0914894", "fetched_at": "2026-08-28T04:05:11.081798+00:00", "kind": "readme", "missing": false, "url": "https://github.com/open-gigaai/giga-world-0"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 55, "longevity": 20, "rhythm": 35}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 281, "days_push": 273, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 41, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}