Introduction
DreamFusion uses a two-dimensional diffusion prior to optimise a three-dimensional representation, extending text-driven synthesis toward objects that can be viewed and lit from different directions.
View full image ↗Figure 1: multiple views, untextured renders and normals of text-generated 3D objects. Source: v1; the image version is distinct from the initial submission date.
Poole, Ben; Jain, Ajay; Barron, Jonathan T.; Mildenhall, Ben · original paper / arXiv · Rights in the paper and depicted works remain with their holders. Research quotation does not establish an open licence; republication rights await independent review.
Source · DreamFusion: Text-to-3D using 2D Diffusion ↗RESEARCH ACCOUNT
DreamFusion uses a two-dimensional diffusion prior to optimise a three-dimensional representation, extending text-driven synthesis toward objects that can be viewed and lit from different directions.
ANTI-AI ARCHIVE · Revised 2026-10-03
What the paper investigates
A pretrained text-to-image model supplies a prior while a neural radiance field is optimised through renders from different viewpoints. The distillation objective converts image-model knowledge into a three-dimensional optimisation signal, avoiding the need to train a large text-labelled 3D diffusion model for this procedure.
Section sources: DreamFusion: Text-to-3D using 2D Diffusion
Its place in generative-art history
Editorial interpretation: the conditions of viewing change when a maker can alter viewpoint, lighting and composition around an object. This provides a production context for digital sculpture and virtual scenes, without automatically classifying research examples as artworks.
Section sources: DreamFusion: Text-to-3D using 2D Diffusion
Reading limits and versions
Multiple views do not guarantee physically correct geometry or production-ready meshes; the archive did not run the model.
Section sources: DreamFusion: Text-to-3D using 2D Diffusion
Sources for this account
Read the arXiv abstract, authors and version history, plus the selected figure/page on PDF page 2; proofs and full experiments were not independently audited.
Read the arXiv abstract, authors and version history, plus the selected figure/page on PDF page 2; proofs and full experiments were not independently audited. The account and translation are AI-assisted, pending independent human review. Section references identify evidence without claiming independent verification of every historical statement.
Continue with a comparative question
DreamFusion: compare methods, control or evaluation conditions with ProlificDreamer.
DreamFusion: compare methods, control or evaluation conditions with Zero-1-to-3.
DreamFusion: compare methods, control or evaluation conditions with From images to video.
DreamFusion: compare methods, control or evaluation conditions with Dream Machine.
Dates & version record
Date displayed for this node: 2022-09-29 · Historical date recorded for the source: 2022-09-29
The timeline uses the initial arXiv submission, distinct from conference publication, model release and the archive addition on 3 October 2026. The account and image refer to v1; its date is recorded in the source history.
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Original sources & further reading
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DreamFusion: Text-to-3D using 2D Diffusion
https://arxiv.org/abs/2209.14988
Current cited URL
Read the arXiv abstract, authors and version history, plus the selected figure/page on PDF page 2; proofs and full experiments were not independently audited.
Provenance, translation & verification
Archive node #198 · Initial research-paper submission; methods, evaluation and production conditions
Research materials & supplement references · 1
Recent research papers: additions and reconciled sources since 2022 · Read the arXiv abstract, authors and version history, plus the selected figure/page on PDF page 2; proofs and full experiments were not independently audited. · 006
Date as recorded: 2022-09-29
DreamFusion: Text-to-3D using 2D Diffusion ↗
DreamFusion: Text-to-3D using 2D Diffusion
Archive account based on the listed research materials, not a full translation of the linked work. AI-assisted translation; independent human review pending.
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Cite this node
ANTI-AI ARCHIVE. DreamFusion: Text-to-3D using 2D Diffusion. Art-history node #198. https://salondesrefuses.cn/en/art-history/329
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