Pith Number

pith:XKRS3Z7R

pith:2023:XKRS3Z7RTYURWBSU2RVUVCP6M3

not attested not anchored not stored refs resolved

Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

Chao Xu, Chong Zeng, Hansheng Chen, Hao Su, Linghao Chen, Minghua Liu, Ruoxi Shi, Xinyue Wei, Zhuoyang Zhang

Targeted conditioning on Stable Diffusion lets a model turn one image into geometrically consistent multi-view outputs.

arxiv:2310.15110 v1 · 2023-10-23 · cs.CV · cs.GR

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\pithnumber{XKRS3Z7RTYURWBSU2RVUVCP6M3}

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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

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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

Zero123++ excels in producing high-quality, consistent multi-view images from a single image, overcoming common issues like texture degradation and geometric misalignment.

C2weakest assumption

That the proposed conditioning and training schemes applied to off-the-shelf Stable Diffusion will reliably produce 3D geometric and texture consistency without new failure modes not captured in the reported examples.

C3one line summary

Zero123++ produces high-quality 3D-consistent multi-view images from a single input by fine-tuning Stable Diffusion with targeted conditioning and training methods.

References

26 extracted · 26 resolved · 12 Pith anchors

[1] ShapeNet: An Information-Rich 3D Model Repository 2015 · arXiv:1512.03012

[2] On the importance of noise scheduling for diffusion models 2023

[3] Objaverse-XL: A Universe of 10M+ 3D Objects 2023 · arXiv:2307.05663

[4] Objaverse: A universe of annotated 3d objects 2023

[5] Efficient diffu- sion training via min-snr weighting strategy 2023

Formal links

2 machine-checked theorem links

Cited by

33 papers in Pith

Materialist: Physically Based Editing Using Single-Image Inverse Rendering

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

SVG360: Editable Multiview Vector Graphics from a Single SVG

Sketch2MinSurf: Vision-Language Guided Generation of Editable Minimal Surfaces from Hand-Drawn Sketches

Stream3D: Sequential Multi-View 3D Generation via Evidential Memory

Receipt and verification

First computed	2026-05-17T23:38:46.429454Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

baa32de7f19e291b0654d46b4a89fe66c20c42a0c169250e495b222a7550c65b

Aliases

arxiv: 2310.15110 · arxiv_version: 2310.15110v1 · doi: 10.48550/arxiv.2310.15110 · pith_short_12: XKRS3Z7RTYUR · pith_short_16: XKRS3Z7RTYURWBSU · pith_short_8: XKRS3Z7R

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/XKRS3Z7RTYURWBSU2RVUVCP6M3 \
  | 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: baa32de7f19e291b0654d46b4a89fe66c20c42a0c169250e495b222a7550c65b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "279ce4061af587502604cef8527fe134497e45b0f384e3acc22d69167d69254d",
    "cross_cats_sorted": [
      "cs.GR"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2023-10-23T17:18:59Z",
    "title_canon_sha256": "28fe4b7db9f6d08983e633f2a43bf8e9673fdc0b955f677d84d6eda1f4a58e54"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2310.15110",
    "kind": "arxiv",
    "version": 1
  }
}