Pith Number

pith:VH4QGEXC

pith:2026:VH4QGEXCDXUBDG7VNJRL3COZ4Q

not attested not anchored not stored refs resolved

DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing

Jinxin Ai, Matthias Nie{\ss}ner, Ziya Erko\c{c}

Personalizing multi-view diffusion models enables text-guided 3D editing with object-level control and preserved consistency.

arxiv:2605.16990 v1 · 2026-05-16 · cs.CV

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\usepackage{pith}
\pithnumber{VH4QGEXCDXUBDG7VNJRL3COZ4Q}

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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

Extensive evaluations across diverse editing scenarios demonstrate that our method successfully transfers the flexibility of 2D personalization to 3D, achieving state-of-the-art edit faithfulness and identity preservation compared to existing baselines.

C2weakest assumption

The approach assumes that rendering orthogonal views and extracting object-level segmentation masks will allow learning of distinct, composable token embeddings that preserve multi-view consistency when combined with editing prompts (stated in the abstract description of the input processing and two-phase optimization).

C3one line summary

DreamEdit3D learns separate token embeddings for segmented object components via two-phase multi-view optimization to enable text-guided 3D editing with consistent image generation and mesh reconstruction.

References

52 extracted · 52 resolved · 14 Pith anchors

[1] In: SIGGRAPH Asia 2023 Conference Papers 2023 · doi:10.1145/3610548.3618154

[2] arXiv preprint arXiv:2408.07009 (2024) 2024

[3] In- stant3dit: Multiview inpainting for fast editing of 3d objects 2024

[4] Betker, J., Goh, G., Jing, L., TimBrooks, Wang, J., Li, L., LongOuyang, Jun- tangZhuang, JoyceLee, YufeiGuo, WesamManassra, PrafullaDhariwal, CaseyChu, YunxinJiao, Ramesh, A.: Improving image generati

[5] In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2022

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-20T00:03:34.824778Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a9f90312e21de8119bf56a62bd89d9e405da5394cda2912c0f7a9c43d56a8574

Aliases

arxiv: 2605.16990 · arxiv_version: 2605.16990v1 · doi: 10.48550/arxiv.2605.16990 · pith_short_12: VH4QGEXCDXUB · pith_short_16: VH4QGEXCDXUBDG7V · pith_short_8: VH4QGEXC

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/VH4QGEXCDXUBDG7VNJRL3COZ4Q \
  | 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: a9f90312e21de8119bf56a62bd89d9e405da5394cda2912c0f7a9c43d56a8574

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "50d81a6eaa10fbb1e7a119b3a3ed7b44dcd81d09b68713a6e7c665771457886a",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-16T13:21:22Z",
    "title_canon_sha256": "a9f84409363b6203c2fbc1e07ccd0d12a7ac15d83ed230f6903a7c69df8a430b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16990",
    "kind": "arxiv",
    "version": 1
  }
}