Pith Number

pith:FMXLIZQA

pith:2023:FMXLIZQA2ART2NQOKAFIIS2ZCQ

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MVDream: Multi-view Diffusion for 3D Generation

Jianglong Ye, Kejie Li, Mai Long, Peng Wang, Xiao Yang, Yichun Shi

A multi-view diffusion model trained on both 2D and 3D data acts as a generalizable 3D prior that improves consistency in text-to-3D generation.

arxiv:2308.16512 v4 · 2023-08-31 · cs.CV

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Claims

C1strongest claim

We demonstrate that such a multi-view diffusion model is implicitly a generalizable 3D prior agnostic to 3D representations. It can be applied to 3D generation via Score Distillation Sampling, significantly enhancing the consistency and stability of existing 2D-lifting methods.

C2weakest assumption

That joint training on 2D and 3D data produces a prior that remains generalizable to novel text prompts and 3D shapes without overfitting to the specific 3D renderings used or sacrificing single-view quality.

C3one line summary

MVDream is a multi-view diffusion model that functions as a generalizable 3D prior, enabling more consistent text-to-3D generation and few-shot 3D concept learning from 2D examples.

References

161 extracted · 161 resolved · 10 Pith anchors

[1] https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 2023

[2] https://sketchfab.com/3d-models/popular 2023

[3] https://huggingface.co/DeepFloyd 2023

[4] https://lumalabs.ai/dashboard/imagine 2023

[5] https://huggingface.co/spaces/lambdalabs/stable-diffusion-image-variations

Formal links

2 machine-checked theorem links

Cited by

44 papers in Pith

Variance Reduction for Expectations with Diffusion Teachers

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

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

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

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

Receipt and verification

First computed	2026-05-17T23:38:53.010570Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2b2eb46600d0233d360e500a844b5914142a957fcb9bd7bee406437a9fd86469

Aliases

arxiv: 2308.16512 · arxiv_version: 2308.16512v4 · doi: 10.48550/arxiv.2308.16512 · pith_short_12: FMXLIZQA2ART · pith_short_16: FMXLIZQA2ART2NQO · pith_short_8: FMXLIZQA

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FMXLIZQA2ART2NQOKAFIIS2ZCQ \
  | 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: 2b2eb46600d0233d360e500a844b5914142a957fcb9bd7bee406437a9fd86469

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2023-08-31T07:49:06Z",
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