Pith Number

pith:KWV56JTV

pith:2023:KWV56JTVDMPYWJ4DGI6WBSDUZ6

not attested not anchored not stored refs resolved

DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation

Gang Zeng, Hang Zhou, Jiawei Ren, Jiaxiang Tang, Ziwei Liu

DreamGaussian generates high-quality textured 3D meshes from a single image in two minutes.

arxiv:2309.16653 v2 · 2023-09-28 · cs.CV

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\usepackage{pith}
\pithnumber{KWV56JTVDMPYWJ4DGI6WBSDUZ6}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

DreamGaussian produces high-quality textured meshes in just 2 minutes from a single-view image, achieving approximately 10 times acceleration compared to existing methods.

C2weakest assumption

Progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks than occupancy pruning in NeRFs, and the subsequent mesh extraction plus UV refinement preserves generation quality without introducing artifacts.

C3one line summary

DreamGaussian creates high-quality textured 3D meshes from single-view images in 2 minutes via generative Gaussian Splatting with mesh extraction and UV refinement.

References

158 extracted · 158 resolved · 9 Pith anchors

[1] ToG , publisher =

[2] Modular Primitives for High-Performance Differentiable Rendering , author =

[3] Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis , author =

[4] Zero-1-to-3: Zero-shot one image to 3d object , author =

[5] Sdedit: Guided image synthesis and editing with stochastic differential equations , author =

Formal links

2 machine-checked theorem links

Cited by

37 papers in Pith

Motion-2-To-3: Leveraging 2D Motion Data for 3D Motion Generations

Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

Diffusion Models are Secretly Zero-Shot 3DGS Harmonizers

ConsDreamer: Advancing Multi-View Consistency for Zero-Shot Text-to-3D Generation

UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models

Receipt and verification

First computed	2026-05-17T23:38:48.215978Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

55abdf26751b1f8b2783323d60c874cfb620d98457a080f542f1e6472f8b3ec9

Aliases

arxiv: 2309.16653 · arxiv_version: 2309.16653v2 · doi: 10.48550/arxiv.2309.16653 · pith_short_12: KWV56JTVDMPY · pith_short_16: KWV56JTVDMPYWJ4D · pith_short_8: KWV56JTV

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/KWV56JTVDMPYWJ4DGI6WBSDUZ6 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 55abdf26751b1f8b2783323d60c874cfb620d98457a080f542f1e6472f8b3ec9

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "359d690044cb76f12cf507ff02a7c4db69db10f39f1ba33bfd238710d69c98b6",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2023-09-28T17:55:05Z",
    "title_canon_sha256": "1014e14e823d5ff27f9cdf733732de8e7ed817fb46ca9d908d186bda109891e9"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2309.16653",
    "kind": "arxiv",
    "version": 2
  }
}