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Paper Citation Record · LEDGER

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation

As of 11 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 5 inbound Pith citation observations for arXiv:2501.05427.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.05427 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:18:45.089456Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T10:57:04.856666Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T16:18:37.505367Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b796d6b1-52d9-4af9-8cab-4dbfdc99e5e7 · outbound

This paper cites GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image

Reference 4

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source=pdf_text observed=2026-08-10T21:18:44.885930Z digest=sha256:1e70b1b8fb5adb955c85a196236823d66af59436f35c5c9dcfd6615fa9fc587f

Observation 713972ba-700a-465b-ba43-a505fa7e8545 · outbound

This paper cites CAT3D: Create Anything in 3D with Multi-View Diffusion Models.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation CAT3D: Create Anything in 3D with Multi-View Diffusion Models

Reference 5

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source=pdf_text observed=2026-08-10T21:18:44.892157Z digest=sha256:8568ead198315d0691d0bfdd92c21426edc643ddfc9b3142d411d40ad1c1fc6f

Observation bfad5e86-81a3-4f65-9534-96ef741e91d6 · outbound

This paper cites GVGEN: Text-to-3D Generation with Volumetric Representation.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation GVGEN: Text-to-3D Generation with Volumetric Representation

Reference 6

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source=pdf_text observed=2026-08-10T21:18:44.901994Z digest=sha256:5884a6a80c113ae6c7581484cf4969f961a0bb3b3a5332b926231ff5f3ab8b6b

Observation 7f4c94f9-4a32-43ce-ba14-83cb3908db95 · outbound

This paper cites LRM: Large Reconstruction Model for Single Image to 3D.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation LRM: Large Reconstruction Model for Single Image to 3D

Reference 8

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source=pdf_text observed=2026-08-10T21:18:44.928391Z digest=sha256:c5bb9d35e14292200ef7ac3f3fb6557ac48e8fbdf81416e002440f983bae5aab

Observation 6654cd73-4ea3-4d6f-8bc0-33fb861b0e50 · outbound

This paper cites 2d gaussian splatting for geometrically accurate radiance fields.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation 2d gaussian splatting for geometrically accurate radiance fields

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-10T21:18:45.949253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T21:18:44.937053Z digest=sha256:426d4843f645220fa92637d6059cd6cc90ed61e2de069beaae14e5df6fa536f3

Observation f9ff4df0-ba78-4a3e-b4cb-ba27e736bc00 · outbound

This paper cites Shap-E: Generating Conditional 3D Implicit Functions.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Shap-E: Generating Conditional 3D Implicit Functions

Reference 10

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-10T21:18:44.944144Z digest=sha256:113fa5ae84974ebe1ba8afba57e0cea47d439dbe3a926d566ab637a50ba50a32

Observation 39372d38-5354-4ed8-9c39-cae726fe389f · outbound

This paper cites Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

Reference 11

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source=pdf_text observed=2026-08-10T21:18:44.950867Z digest=sha256:5b70bc28df3ef6f2100e1121f23e066b9a18b3a893ddaed4c4b20bb0514619c4

Observation 354f91e3-4411-49cd-9c69-8c1e4dc0c4c7 · outbound

This paper cites Ln3diff: Scalable latent neural fields diffusion for speedy 3d generation.arXiv preprint arXiv:2403.12019,.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Ln3diff: Scalable latent neural fields diffusion for speedy 3d generation.arXiv preprint arXiv:2403.12019,

Reference 12

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source=pdf_text observed=2026-08-10T21:18:44.957636Z digest=sha256:1a582d8fbfc5ceac88c85bb9bb727f481ad3d01427180d192822f273a163fa31

Observation 20c2c3cb-b08d-4ad6-8521-ca1f2498e620 · outbound

This paper cites Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model

Reference 13

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source=pdf_text observed=2026-08-10T21:18:44.963368Z digest=sha256:be93acf4312e6632aa997fd03b3c19f4dd6275448a3cd300f728d55d96ac1b2f

Observation 440ded59-43cb-4e22-99da-98f20148aca5 · outbound

This paper cites Part123: Part-aware 3d reconstruction from a single-view image.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Part123: Part-aware 3d reconstruction from a single-view image

Reference 14

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raw_fallback, observed 2026-08-10T21:18:45.932037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T21:18:44.970273Z digest=sha256:74e28b06a6d460b66cccd285569bd38998478a0b1b67b7daedef32e26b9684e8

Observation 624f1196-7b90-430b-9126-eafae05c043e · outbound

This paper cites One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion

Reference 15

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source=pdf_text observed=2026-08-10T21:18:44.976390Z digest=sha256:c785002d4dff1cf49cb4af6628865e3834fa45594fc28c28a6f4c9b91e2ec3be

Observation acc432d1-bdb5-4d98-8a3e-684c60365699 · outbound

This paper cites Point-E: A System for Generating 3D Point Clouds from Complex Prompts.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Point-E: A System for Generating 3D Point Clouds from Complex Prompts

Reference 16

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source=pdf_text observed=2026-08-10T21:18:44.988679Z digest=sha256:88fa29ed86a0cb9ed86c9819a68a6d345ae0b682c716887d2147e8491cd9fb52

Observation b44d58c9-63f1-43e3-86eb-d5e3959c8cd7 · outbound

This paper cites DreamFusion: Text-to-3D using 2D Diffusion.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation DreamFusion: Text-to-3D using 2D Diffusion

Reference 17

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source=pdf_text observed=2026-08-10T21:18:44.994908Z digest=sha256:4cc65a3caefa012515cf0ba5bb859b61c749ad2177769a6a7bddb5d6d7968bde

Observation f0940014-8581-4bf3-8d5a-4094c57b7df4 · outbound

This paper cites MVDream: Multi-view Diffusion for 3D Generation.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation MVDream: Multi-view Diffusion for 3D Generation

Reference 18

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source=pdf_text observed=2026-08-10T21:18:45.001737Z digest=sha256:80bd16fbc860da63ba9ef105f45a1a12cafd3c0125ac377cb51faa852d9918e0

Observation e28daf65-cd5c-4fa8-b9c6-0057d8106ba3 · outbound

This paper cites Consistency Models.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Consistency Models

Reference 19

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source=pdf_text observed=2026-08-10T21:18:45.007841Z digest=sha256:85eaca16431ff830827ed952a24e8cc715af5519fb996bae1eb5bb6192d57cd9

Observation 860f0452-5681-41ea-8785-52c526eb327a · outbound

This paper cites DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation

Reference 20

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source=pdf_text observed=2026-08-10T21:18:45.013243Z digest=sha256:825f13e14346b97309acd767e341aded7de71f06c10759b486da0e791b03b01b

Observation 8a714192-5caa-4e4c-a651-a901bd975163 · outbound

This paper cites LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation

Reference 21

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source=pdf_text observed=2026-08-10T21:18:45.019536Z digest=sha256:2ed8d7b83ec1ba684adb8421d1fa5b9d313b48f89264fdb51d82c7769462e4e9

Observation b8487e1a-bc15-40d5-a2b8-21935dc5ed10 · outbound

This paper cites TripoSR: Fast 3D Object Reconstruction from a Single Image.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation TripoSR: Fast 3D Object Reconstruction from a Single Image

Reference 22

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source=pdf_text observed=2026-08-10T21:18:45.035651Z digest=sha256:7e3543cfa69426137ee2e711c027765cef7c9e1e0e50461114f0282c2aef026d

Observation 0f4e2330-06ab-4627-9300-962b9cc311f1 · outbound

This paper cites GECO: Generative Image-to-3D within a SECOnd.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation GECO: Generative Image-to-3D within a SECOnd

Reference 23

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source=pdf_text observed=2026-08-10T21:18:45.042945Z digest=sha256:392e9cc32b7d1c96426de223cf6e13c891b5fe366fd7fd23e2e5dd7c3690499f

Observation c8e7a808-9e59-4c61-a382-5441b722c46e · outbound

This paper cites ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation

Reference 24

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source=pdf_text observed=2026-08-10T21:18:45.048848Z digest=sha256:1c8554e21a17e6442132619379f03421590a9e897a0c9a09a17f4797ad92b3ee

Observation 8220a0c7-e5e7-4f1c-a6c3-008fb90e9901 · outbound

This paper cites InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models

Reference 25

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source=pdf_text observed=2026-08-10T21:18:45.055266Z digest=sha256:a78008720d579dd85db725108a1ad112f573a0d2b470a249366f7ea9f0bbbafb

Observation 26133e6c-f0f4-4121-92a4-01cd84a09005 · outbound

This paper cites DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model

Reference 26

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source=pdf_text observed=2026-08-10T21:18:45.060545Z digest=sha256:45696e68513564fbdb98971dfaf6c269845eb72167dde88098f981e32c2b11a4

Observation 5589f610-afae-412d-9cbd-3b653f0d528e · outbound

This paper cites An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion

Reference 27

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source=pdf_text observed=2026-08-10T21:18:45.066151Z digest=sha256:023f7159510955fff756e44f46ec7119180ddfb5c0ed057281a1a414691b2181

Observation eea81d25-f465-4d1c-a6c5-960b7dd74e18 · outbound

This paper cites 3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation 3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models

Reference 28

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source=pdf_text observed=2026-08-10T21:18:45.072084Z digest=sha256:6b03d573d04fd02e03fdc9d8e0ce31ea3a3680d0d4b7d7a8ce95209b36a21207

Observation a97a9450-1c58-46fc-a408-318fbdc34fb0 · outbound

This paper cites The training objective is to compare the splatter renderings with ground truth images using MSE and LPIPS loss.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation The training objective is to compare the splatter renderings with ground truth images using MSE and LPIPS loss

Reference 31

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raw_fallback, observed 2026-08-10T21:18:45.913595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T21:18:45.089456Z digest=sha256:1fa6659583f0bbed0186dedc487d0f5c0c406ef3114293e5c0aac034873a02db

Observation 3ed72d4f-0f52-486c-b3dc-64ce0da5272a · outbound

This paper cites Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation

Reference 2018

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source=pdf_text observed=2026-08-10T21:18:45.077414Z digest=sha256:f67770475e4784171d9bfb6cf61c884a209734e74b22e5efd04aed03a9291972

Observation ba668108-45d6-4041-bced-810e4dffdaea · outbound

This paper cites 3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation 3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors

Reference 2020

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source=pdf_text observed=2026-08-10T21:18:44.909343Z digest=sha256:2e8d08d5974204c44f1e71ccf6ad101ac1b4c53192359ff54534851543929e58

Observation 4374915b-736d-47b9-9e60-3be8046e6553 · outbound

This paper cites Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers

Reference 2021

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source=pdf_text observed=2026-08-10T21:18:45.083440Z digest=sha256:f42fa4ee5a92687553e47491176d85169659261155c864283bcf79dabf587971

Observation a019b4ae-074b-4b91-87d1-c0c8773ed006 · outbound

This paper cites Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface Representation.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface Representation

Reference 2022

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source=pdf_text observed=2026-08-10T21:18:44.879114Z digest=sha256:d5da31ae43bf160cb078fcba3aca0027cbcb5a90fdd17106862ac9cc0f23d41a

Observation 095aec88-41d7-43b1-bb3d-a0ce5f90c742 · outbound

This paper cites Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction

Reference 2023

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source=pdf_text observed=2026-08-10T21:18:44.865323Z digest=sha256:a91e98a4a0fe079206c45bd779eda484e1a737f0dba962f0dedada6184f217da

Observation 34906667-b18d-4792-a29e-44a6041efc73 · outbound

This paper cites Google scanned objects: A high-quality dataset of 3d scanned household items.

Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation Google scanned objects: A high-quality dataset of 3d scanned household items

Reference 2024

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source=pdf_text observed=2026-08-10T21:18:44.871923Z digest=sha256:c7ab74e1d441a8326848731e5e96bd4c0f088787dc45876187de90aab9ea7aae

Pith citing papers

Observation 508bcaa3-049d-410f-84ca-c0a3d91ed93a · inbound

Rectifying Mask via Entropy for Distractor-Free 3DGS in Ambiguous Scenarios cites this paper.

Rectifying Mask via Entropy for Distractor-Free 3DGS in Ambiguous Scenarios Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation

Reference 34

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metadata mismatch
arxiv_id, observed 2026-06-30T07:14:21.313031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-30T07:09:35.737133Z digest=sha256:c8f4962ec414d239c8c4a925b9caf4ec93fdfbee20239406179ac6373a1fec08

Observation 52bda9b5-a840-445b-be9d-d449707849e3 · inbound

Rectifying Mask via Entropy for Distractor-Free 3DGS in Ambiguous Scenarios cites this paper.

Rectifying Mask via Entropy for Distractor-Free 3DGS in Ambiguous Scenarios Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation

Reference 34

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source=pdf_text observed=2026-07-12T10:57:04.856666Z digest=sha256:a5326f75bd60a04e1c68716154ccf7f55be5b9ad6819d98e7d29f36ce2521709

Observation 54688116-3873-42cd-bd6d-008525ba7928 · inbound

PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation cites this paper.

PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation

Reference 28

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verified exact
arxiv_id, observed 2026-07-03T16:18:37.506749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-03T16:13:41.928049Z digest=sha256:d0ed8d959f7c1f7ae14bf72baa2418328897274a6014ee8b7a5165e69a6de6be

Observation d3608123-9d7e-44f0-82be-4a046e7a8143 · inbound

PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation cites this paper.

PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation

Reference 29

Resolution
unresolved
no resolver link, observed 2026-07-12T08:36:06.846852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T08:36:06.846852Z digest=sha256:0a833c10b7e957b513a4ede6a8441c52441f22e0b4c22dd1e6d63f3051ad1e75

Observation ee20e808-0729-451e-8e1c-394faee8491b · inbound

AdaptiveSplat:Texture Aware Controllable 3D Gaussian Allocation for Feed-Forward Reconstruction cites this paper.

AdaptiveSplat:Texture Aware Controllable 3D Gaussian Allocation for Feed-Forward Reconstruction Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation

Reference 40

Resolution
unresolved
no resolver link, observed 2026-07-11T20:35:53.491714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T20:35:53.491714Z digest=sha256:85887bfea0f5b11d037f686f48d0c224ba6bca79c49250438021021c56d155e6