{
  "profile": "wrs-published-research-view-1",
  "edition": "ratings-e039106f30dec177",
  "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS",
  "entry": {
    "id": "poole-et-al-dreamfusion-score-distillation-2022",
    "title": "Poole et al.: DreamFusion diffusion-score distillation for text-to-3D",
    "summary": "Used a pretrained two-dimensional text-image diffusion prior to optimize renderings of a three-dimensional representation through score distillation, avoiding a separately trained text-3D generator.",
    "boundary": "NeRF and image diffusion are inherited. No3D training data in this optimization does not mean the2D prior lacks object knowledge. View inconsistency, optimization bias and geometry limitations remain; generated assets are not physically verified.",
    "area": "Text-conditioned 3D optimization",
    "topic": "Artificial intelligence",
    "year": 2022,
    "lane": "HUMAN",
    "result": "ALGORITHM",
    "package_id": "poole-et-al-dreamfusion-score-distillation-2022",
    "target": {
      "record_id": "urn:uuid:4abfde31-8f8f-4df6-ad1c-8ee058186c9d",
      "version": 1,
      "content_hash": "sha256:63c740437a4adb7eefdc54d1dd987515d47033bded3465ba425d1ce4588f7a03"
    },
    "humans": [
      "Ben Poole",
      "Ajay Jain",
      "Jonathan T. Barron",
      "Ben Mildenhall"
    ],
    "authors_complete": true,
    "human_role_note": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.",
    "agents": [],
    "related": [],
    "subresults": [],
    "scientific_assessment": {
      "source_review": "SOURCE_CURATED",
      "scientific_review": "NOT_REVIEWED_BY_WRS",
      "formal_replay": "NOT_RUN",
      "provider_attestation": "NONE"
    },
    "sources": [
      {
        "id": "cur20261015-arxiv-2209-14988v1",
        "title": "DreamFusion: Text-to-3D using 2D Diffusion",
        "url": "https://arxiv.org/abs/2209.14988v1"
      },
      {
        "id": "cur20261015-support-poole-et-al-dreamfusion-score-distillation-2022",
        "title": "Poole et al.: DreamFusion diffusion-score distillation for text-to-3D — author/institutional project",
        "url": "https://dreamfusion3d.github.io/"
      }
    ],
    "points": "145000",
    "published_grade": {
      "id": "poole-et-al-dreamfusion-score-distillation-2022",
      "entry_id": "poole-et-al-dreamfusion-score-distillation-2022",
      "band": "A",
      "modifier": null,
      "target": {
        "record_id": "urn:uuid:4abfde31-8f8f-4df6-ad1c-8ee058186c9d",
        "version": 1,
        "content_hash": "sha256:63c740437a4adb7eefdc54d1dd987515d47033bded3465ba425d1ce4588f7a03"
      },
      "what_changed": "Used a pretrained two-dimensional text-image diffusion prior to optimize renderings of a three-dimensional representation through score distillation, avoiding a separately trained text-3D generator.",
      "scope": "Used a pretrained two-dimensional text-image diffusion prior to optimize renderings of a three-dimensional representation through score distillation, avoiding a separately trained text-3D generator. NeRF and image diffusion are inherited. No3D training data in this optimization does not mean the2D prior lacks object knowledge. View inconsistency, optimization bias and geometry limitations remain; generated assets are not physically verified.",
      "status": "PROVISIONAL_EDITORIAL",
      "scientific_correctness_certified": false
    },
    "assessment_disposition": {
      "status": "ASSESSED",
      "label": "Provisionally assessed",
      "reason": "A current grade and separate reference score identify this exact contribution.",
      "target": {
        "record_id": "urn:uuid:4abfde31-8f8f-4df6-ad1c-8ee058186c9d",
        "version": 1,
        "content_hash": "sha256:63c740437a4adb7eefdc54d1dd987515d47033bded3465ba425d1ce4588f7a03"
      },
      "edition": "ratings-e039106f30dec177"
    }
  },
  "version": {
    "kind": "PUBLIC_RECORD_VIEW",
    "previous": null,
    "published_at": "2026-10-16T00:00:00Z",
    "publisher": {
      "principal_id": "urn:uuid:80116931-785b-40a2-a24b-9d59db0c7e33",
      "role": "WRS_EDITORIAL_PUBLICATION"
    },
    "record": {
      "contributors": [
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Ben Poole",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "DreamFusion: Text-to-3D using 2D Diffusion",
            "url": "https://arxiv.org/abs/2209.14988v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Ajay Jain",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "DreamFusion: Text-to-3D using 2D Diffusion",
            "url": "https://arxiv.org/abs/2209.14988v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Jonathan T. Barron",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "DreamFusion: Text-to-3D using 2D Diffusion",
            "url": "https://arxiv.org/abs/2209.14988v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Ben Mildenhall",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "DreamFusion: Text-to-3D using 2D Diffusion",
            "url": "https://arxiv.org/abs/2209.14988v1"
          }
        }
      ],
      "evidence": [
        {
          "description": "Bibliographic/stated-result source; not a WRS proof verification",
          "id": "source-0",
          "kind": "source_reference",
          "source": {
            "citation": "DreamFusion: Text-to-3D using 2D Diffusion",
            "url": "https://arxiv.org/abs/2209.14988v1"
          }
        },
        {
          "description": "Bibliographic/stated-result source; not a WRS proof verification",
          "id": "source-1",
          "kind": "source_reference",
          "source": {
            "citation": "Poole et al.: DreamFusion diffusion-score distillation for text-to-3D — author/institutional project",
            "url": "https://dreamfusion3d.github.io/"
          }
        }
      ],
      "origin": "HUMAN_LEGACY",
      "record_profile": "wrs-local-research-notes-0.1",
      "record_type": "research_note",
      "rrp_version": "0.1",
      "scope": {
        "description": "NeRF and image diffusion are inherited. No3D training data in this optimization does not mean the2D prior lacks object knowledge. View inconsistency, optimization bias and geometry limitations remain; generated assets are not physically verified.",
        "domain": "Text-conditioned 3D optimization"
      },
      "sources": [
        {
          "citation": "DreamFusion: Text-to-3D using 2D Diffusion",
          "url": "https://arxiv.org/abs/2209.14988v1"
        },
        {
          "citation": "Poole et al.: DreamFusion diffusion-score distillation for text-to-3D — author/institutional project",
          "url": "https://dreamfusion3d.github.io/"
        }
      ],
      "statement": "# Poole et al.: DreamFusion diffusion-score distillation for text-to-3D\n\nUsed a pretrained two-dimensional text-image diffusion prior to optimize renderings of a three-dimensional representation through score distillation, avoiding a separately trained text-3D generator.\n\n## Scientific scope\n\nNeRF and image diffusion are inherited. No3D training data in this optimization does not mean the2D prior lacks object knowledge. View inconsistency, optimization bias and geometry limitations remain; generated assets are not physically verified.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nBen Poole; Ajay Jain; Jonathan T. Barron; Ben Mildenhall\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [DreamFusion: Text-to-3D using 2D Diffusion](https://arxiv.org/abs/2209.14988v1)\n- [Poole et al.: DreamFusion diffusion-score distillation for text-to-3D — author/institutional project](https://dreamfusion3d.github.io/)\n",
      "summary": "Used a pretrained two-dimensional text-image diffusion prior to optimize renderings of a three-dimensional representation through score distillation, avoiding a separately trained text-3D generator.",
      "title": "Poole et al.: DreamFusion diffusion-score distillation for text-to-3D"
    },
    "record_id": "urn:uuid:4abfde31-8f8f-4df6-ad1c-8ee058186c9d",
    "version": 1,
    "source_ref": {
      "record_id": "urn:uuid:4abfde31-8f8f-4df6-ad1c-8ee058186c9d",
      "version": 1,
      "content_hash": "sha256:63c740437a4adb7eefdc54d1dd987515d47033bded3465ba425d1ce4588f7a03"
    },
    "export_view": "wrs-public-editorial-view-1",
    "public_view_hash": "sha256:d664b16468c8e1eebc1e8e80dac92a604c42e5da790689680130b234c853055c"
  },
  "target": {
    "record_id": "urn:uuid:4abfde31-8f8f-4df6-ad1c-8ee058186c9d",
    "version": 1,
    "content_hash": "sha256:63c740437a4adb7eefdc54d1dd987515d47033bded3465ba425d1ce4588f7a03"
  },
  "published_grade": {
    "profile": "wrs-published-grade-1",
    "edition": "2026-10-28.1",
    "id": "poole-et-al-dreamfusion-score-distillation-2022",
    "entry_id": "poole-et-al-dreamfusion-score-distillation-2022",
    "revision": 1,
    "title": "Poole et al.: DreamFusion diffusion-score distillation for text-to-3D",
    "context": "Text-conditioned 3D optimization",
    "track": "Human-designed research method or resource",
    "band": "A",
    "modifier": null,
    "status": "PROVISIONAL_EDITORIAL",
    "assessed_on": "2026-10-16",
    "evidence_cutoff": "2026-10-16",
    "achievement_state": "SUPPORTED_SCOPED_RESULT",
    "what_changed": "Used a pretrained two-dimensional text-image diffusion prior to optimize renderings of a three-dimensional representation through score distillation, avoiding a separately trained text-3D generator.",
    "scope": "Used a pretrained two-dimensional text-image diffusion prior to optimize renderings of a three-dimensional representation through score distillation, avoiding a separately trained text-3D generator. NeRF and image diffusion are inherited. No3D training data in this optimization does not mean the2D prior lacks object knowledge. View inconsistency, optimization bias and geometry limitations remain; generated assets are not physically verified.",
    "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. NeRF and image diffusion are inherited. No3D training data in this optimization does not mean the2D prior lacks object knowledge. View inconsistency, optimization bias and geometry limitations remain; generated assets are not physically verified.",
    "references": [
      {
        "title": "DreamFusion: Text-to-3D using 2D Diffusion",
        "url": "https://arxiv.org/abs/2209.14988v1",
        "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification."
      },
      {
        "title": "Poole et al.: DreamFusion diffusion-score distillation for text-to-3D — author/institutional project",
        "url": "https://dreamfusion3d.github.io/",
        "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification."
      }
    ],
    "roles": [
      {
        "who": "Source-listed authors and technical contributors",
        "credit": "SCIENCE",
        "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities."
      }
    ],
    "target": {
      "record_id": "urn:uuid:4abfde31-8f8f-4df6-ad1c-8ee058186c9d",
      "version": 1,
      "content_hash": "sha256:63c740437a4adb7eefdc54d1dd987515d47033bded3465ba425d1ce4588f7a03"
    },
    "scientific_correctness_certified": false,
    "independent_expert_calibration": false,
    "method": "PROPRIETARY",
    "points": "145000"
  },
  "canonical_agent_submission": false,
  "scientific_verification_awarded": false,
  "reader_guide": null,
  "reading_status": "SOURCE_LINKED_NOTE"
}