Pith Number

pith:X4TY25NQ

pith:2023:X4TY25NQHW46BYU56E7GVIXMS4

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Shap-E: Generating Conditional 3D Implicit Functions

Alex Nichol, Heewoo Jun

Shap-E generates parameters of implicit functions for 3D assets directly from text prompts.

arxiv:2305.02463 v1 · 2023-05-03 · cs.CV · cs.LG

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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

When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds.

C2weakest assumption

The encoder stage can deterministically map arbitrary 3D assets into implicit-function parameters with negligible information loss, allowing the diffusion stage to operate in a faithful latent space.

C3one line summary

Shap-E encodes 3D assets into implicit function parameters then uses a conditional diffusion model to generate new ones from text, enabling fast multi-representation 3D asset creation.

References

74 extracted · 74 resolved · 33 Pith anchors

[1] Learning Representations and Generative Models for 3D Point Clouds 2017 · arXiv:1707.02392

[2] MusicLM: Generating Music From Text 2023 · arXiv:2301.11325

[3] Sine: Semantic-driven image-based nerf editing with prior-guided editing ﬁeld 2023

[4] Gaudi: A neural architect for immersive 3d scene generation 2022

[5] Audiolm: a language modeling approach to audio generation 2022

Formal links

2 machine-checked theorem links

Cited by

35 papers in Pith

BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion

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

XARP Tools: An Extended Reality Platform for Humans and AI Agents

SVG360: Editable Multiview Vector Graphics from a Single SVG

Native and Compact Structured Latents for 3D Generation

Receipt and verification

First computed	2026-05-17T23:38:47.434855Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

bf278d75b03db9e0e29df13e6aa2ec973e27313d9faa82730e290abc69d0640a

Aliases

arxiv: 2305.02463 · arxiv_version: 2305.02463v1 · doi: 10.48550/arxiv.2305.02463 · pith_short_12: X4TY25NQHW46 · pith_short_16: X4TY25NQHW46BYU5 · pith_short_8: X4TY25NQ

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/X4TY25NQHW46BYU56E7GVIXMS4 \
  | 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: bf278d75b03db9e0e29df13e6aa2ec973e27313d9faa82730e290abc69d0640a

Canonical record JSON

{
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    "abstract_canon_sha256": "0d9d62f9c470ef444012dea373b4075f2ee687555e566b89393a360a207b5a22",
    "cross_cats_sorted": [
      "cs.LG"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2023-05-03T23:59:13Z",
    "title_canon_sha256": "849263f58355d0a3194b14c6015d5d2a4ce1ca230f9388ad0c4f617cac30d80a"
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  "source": {
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}