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Paper Citation Record · LEDGER

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.24245.

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

pith.paper-citation-record.v1
2505.24245 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:33:38.368887Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

  • verified exact1
  • verified fuzzy16
  • unresolved24
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ac94f1d8-26b4-4e5b-bd0e-27378c5e0056 · outbound

This paper cites GPT-4 Technical Report.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-07T12:33:34.854572Z digest=sha256:582b15482774cc0134a35e3662d8ff480c3e6331059431b92cd7584c72867f51

Observation fc9c0cdf-baa6-4b43-bfe7-bbb6c0b7abd8 · outbound

This paper cites Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736,.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736,

Reference 2

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source=pdf_text observed=2026-08-07T12:33:34.991095Z digest=sha256:e628bb009f67084fb2b2c900cd569e07d29bfc6a018bb0f51942fa800abdb803

Observation 9c445e6c-2b1a-4abc-a2a4-1333cf76f69c · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework ShapeNet: An Information-Rich 3D Model Repository

Reference 3

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source=pdf_text observed=2026-08-07T12:33:35.147821Z digest=sha256:9a78a6e3710bda7158e781e9bdcd0911293f237ad5a4c9a6166f10c03d3ca0c8

Observation 17e07eeb-047b-4a72-be1a-3b9a93e0a164 · outbound

This paper cites MeshXL: Neural Coordinate Field for Generative 3D Foundation Models.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework MeshXL: Neural Coordinate Field for Generative 3D Foundation Models

Reference 4

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source=pdf_text observed=2026-08-07T12:33:35.284816Z digest=sha256:27fbee39581172183910c93b5f7930810dccc53d0a08050fc7d9751166b744aa

Observation 57d24baa-757d-4ad8-8621-23b055defbc0 · outbound

This paper cites Text-to-3d using gaussian splatting.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Text-to-3d using gaussian splatting

Reference 5

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raw_fallback, observed 2026-08-07T12:33:41.406199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:35.426270Z digest=sha256:150409b95708d305acf6a8ff6e964713b59af3077dd97e1a3cfdad86ab7996bd

Observation 2237ab6c-7e09-45de-bd10-25c8fa0cd992 · outbound

This paper cites Sdfusion: Multimodal 3d shape completion, reconstruction, and generation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Sdfusion: Multimodal 3d shape completion, reconstruction, and generation

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:35.595436Z digest=sha256:06761e2f0245e738018753500139b506fcc52ef80bb3eae11ab658b9e918d3e1

Observation acfb5287-8caa-4076-a3e6-0645244eeb67 · outbound

This paper cites Objaverse: A universe of annotated 3d objects.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Objaverse: A universe of annotated 3d objects

Reference 7

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source=pdf_text observed=2026-08-07T12:33:35.693755Z digest=sha256:f4cba7b17452ed22c2ab6bff444db3b0434873e5598ed75066a66b856d039836

Observation 5bb6ce1c-8734-409c-8ecf-c6632d8ccc7a · outbound

This paper cites Masked autoencoders are scalable vision learners.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Masked autoencoders are scalable vision learners

Reference 8

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raw_fallback, observed 2026-08-07T12:33:41.118050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:35.758894Z digest=sha256:ed3a653a1e0fa3640f001c115f7e0c308a404c21092b243c0d1fb4ee16b21347

Observation e04075b3-eaa6-4af1-8705-ce0c097dd671 · outbound

This paper cites DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation

Reference 9

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T12:33:35.822212Z digest=sha256:a270ec17d4e46d41b047117cd35328f60db400ee43a73888b46ac260a4e5cb10

Observation 3bbe6e99-3960-4208-9d45-eb4b158e0da3 · outbound

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

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Shap-E: Generating Conditional 3D Implicit Functions

Reference 10

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source=pdf_text observed=2026-08-07T12:33:35.918512Z digest=sha256:ae4f6f0dd4571740961c5f22e3014b915645aeb23bb1377671e2222ca1972052

Observation 00f3cf73-ba51-4161-a09e-941db918af30 · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.ACM Trans.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework 3d gaussian splatting for real-time radiance field rendering.ACM Trans

Reference 11

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source=pdf_text observed=2026-08-07T12:33:36.005351Z digest=sha256:d830bb6607b074757b02a5cb1cf472e9f90d5a05e1d68dffe4ebff153bb44c8d

Observation 208ce92a-4569-4530-b08b-bd7a29cb40a2 · outbound

This paper cites Segment any- thing.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Segment any- thing

Reference 12

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source=pdf_text observed=2026-08-07T12:33:36.053994Z digest=sha256:cf48cfb982b84a889eb5a890ffd9b95efe9fa9b4d8ff310bb7990ca686f3a141

Observation d9d2e18f-7b04-48a7-bd52-de60e38c468e · outbound

This paper cites Diffusion- sdf: Text-to-shape via voxelized diffusion.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Diffusion- sdf: Text-to-shape via voxelized diffusion

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:33:40.983218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:36.146461Z digest=sha256:183f1770faec329acf982e80b9a340c02687d6d8095f734f436843e635b47def

Observation dedbf24c-5b3a-47e3-9845-6dfd5df62e22 · outbound

This paper cites Autoregressive Image Generation without Vector Quantization.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Autoregressive Image Generation without Vector Quantization

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:36.235172Z digest=sha256:bc14a841df1dbec8e4b1e01486ce2451f482b0eebee4a098adffe88504d037e7

Observation aa8df983-4bf5-4b52-a281-ffdf2d6e18b1 · outbound

This paper cites Magic3d: High-resolution text-to-3d content creation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Magic3d: High-resolution text-to-3d content creation

Reference 15

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:36.300200Z digest=sha256:8959e2f0b54f09dbeec23e8582ea8cc217d1949bff33fb6ec9ae78d74c23e34e

Observation 327095cf-11d8-467d-ac96-8cc0118edb4e · outbound

This paper cites FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Reference 16

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local_arxiv, observed 2026-08-07T12:33:38.610708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:36.378809Z digest=sha256:b8b260b2bc02167d83512b3a13e195d46a28f320106a4b1631fdf2427dc8680a

Observation 57568344-f8a1-4cee-9aa8-c9327e5b38bb · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view syn- thesis.Communications of the ACM, 65(1):99–106, 2021.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Nerf: Representing scenes as neural radiance fields for view syn- thesis.Communications of the ACM, 65(1):99–106, 2021

Reference 17

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source=pdf_text observed=2026-08-07T12:33:36.480926Z digest=sha256:ada697d3b3612252d1420566ab762b20f137e3e5f4bb575dae667492811f75f3

Observation da0c2d04-8ac2-43d8-bc44-4443fb563095 · outbound

This paper cites Autosdf: Shape priors for 3d comple- tion, reconstruction and generation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Autosdf: Shape priors for 3d comple- tion, reconstruction and generation

Reference 18

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raw_fallback, observed 2026-08-07T12:33:40.605354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:36.542130Z digest=sha256:900e184f1c9c3b3d1818275ce316e24d95188f41070a092a95103e8d28b6c974

Observation c5168fa8-201e-43a0-944d-880c5587a814 · outbound

This paper cites Polygen: An autoregressive generative model of 3d meshes.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Polygen: An autoregressive generative model of 3d meshes

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:36.616195Z digest=sha256:f723fe8b76f103c3e46ee71f3ddd868741199917155fe54be67082d0c387fec0

Observation 73d07521-8dda-4498-903a-0acc8c80afdd · outbound

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

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Point-E: A System for Generating 3D Point Clouds from Complex Prompts

Reference 20

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source=pdf_text observed=2026-08-07T12:33:36.695604Z digest=sha256:e4520fb58abd113a450ba2e12422e6c0aa7d1dc88ee0b898b0e12abecfca26ca

Observation 226b3bd2-9914-4aa1-9846-a04f79dd6aba · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework DINOv2: Learning Robust Visual Features without Supervision

Reference 21

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source=pdf_text observed=2026-08-07T12:33:36.756072Z digest=sha256:36d5dca71d680cf832b4cf4691ac888ff97286c896461aecb42ac815dad4034e

Observation 72b2db93-28b6-46cd-b06a-f444cc5ef701 · outbound

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

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework DreamFusion: Text-to-3D using 2D Diffusion

Reference 22

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source=pdf_text observed=2026-08-07T12:33:36.841766Z digest=sha256:d4703a898a946b0cbe3268318170f6a42704b5c33b30b03d568404ba6d82bc99

Observation bad6702b-8dc9-4f54-b718-8a6db91abeb2 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Learning transferable visual models from natural language supervi- sion

Reference 23

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source=pdf_text observed=2026-08-07T12:33:36.932320Z digest=sha256:dba20347ca86dc267727b08af30748a62e0e38ea5e48c303c5365eccb2c8f10e

Observation 687f1646-a635-4c6c-86ff-ec7cf0861352 · outbound

This paper cites Zero-shot text-to-image generation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Zero-shot text-to-image generation

Reference 24

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raw_fallback, observed 2026-08-07T12:33:40.289153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:37.036565Z digest=sha256:962dfb690e8a947ff0172a3c1ae03c45a1b8e0f5430045503eb36deece409133

Observation 9f5cc8b7-3b0e-4c0e-8f98-321995e2d3c9 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework High-resolution image synthesis with latent diffusion models

Reference 25

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:37.110644Z digest=sha256:1391e97c27f7d129e7daa314624edcae63df41de1ada4c74e5d466cff965a14c

Observation 63366473-daa8-4f29-b3cd-bffc1a179e68 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022

Reference 26

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source=pdf_text observed=2026-08-07T12:33:37.190675Z digest=sha256:8890023af2d50d6a7295aca5629a551dce5db13fe39cb6bc79f9926b1ef77a23

Observation d29d2450-72a9-4999-b891-6bc0eff2eee2 · outbound

This paper cites Meshgpt: Generating triangle meshes with decoder-only transformers.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Meshgpt: Generating triangle meshes with decoder-only transformers

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-07T12:33:40.115863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:37.271378Z digest=sha256:40d401206189693da92fbd0a16656234f4f97a58a7faa76ed91badc69cf7d0a2

Observation 41822879-05f7-4508-a8d7-6c198db6fc75 · outbound

This paper cites Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior

Reference 28

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source=pdf_text observed=2026-08-07T12:33:37.342613Z digest=sha256:8604152fba20f2187834e8cbaa19fe352b92595dea823f3c660d2fb4e67bad6f

Observation 2d4d5483-f788-49f6-9f6d-be072f4c38d8 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework LLaMA: Open and Efficient Foundation Language Models

Reference 29

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source=pdf_text observed=2026-08-07T12:33:37.442699Z digest=sha256:125fb77702588831a060830fd8d9a2743a3a2f8f44d16e0ef264710b857814a2

Observation 98fdb654-897f-4ac9-9291-d08f21299333 · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 2017.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Attention is all you need.Advances in Neural Information Processing Systems, 2017

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:37.523208Z digest=sha256:c22a65b5b546b57c4d0353c9aaeb7f61659f6e03eafa46a35952710a0f492bc6

Observation 85e0ad5c-177f-4b2e-ba1d-068861af7717 · outbound

This paper cites Hd- fusion: Detailed text-to-3d generation leveraging multiple noise estimation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Hd- fusion: Detailed text-to-3d generation leveraging multiple noise estimation

Reference 31

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raw_fallback, observed 2026-08-07T12:33:39.964739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:37.613870Z digest=sha256:5021bda9fc93f46b467872a602faf1e22bdbddf0b333ffae6db6fb3500cbff74

Observation e1c5edcd-23a3-49ae-8406-8b8553df24be · outbound

This paper cites Disn: Deep implicit surface network for high-quality single-view 3d reconstruction.Ad- vances in neural information processing systems, 32, 2019.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Disn: Deep implicit surface network for high-quality single-view 3d reconstruction.Ad- vances in neural information processing systems, 32, 2019

Reference 32

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raw_fallback, observed 2026-08-07T12:33:39.778378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:37.693451Z digest=sha256:be2f6daf1b23038314a950e957ce72925e8aa3a72755b15cd1aa9c797caf68c2

Observation eee02389-cf48-41fb-8fe0-c7a476b4611e · outbound

This paper cites Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:37.768481Z digest=sha256:ce8413dc8a50e34f0651fc132be375baa35e0673a9527ce731c9ac58d1560b59

Observation af029760-1e12-476b-8a70-9e9892f3da45 · outbound

This paper cites Points-to-3d: Bridging the gap be- tween sparse points and shape-controllable text-to-3d gener- ation.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Points-to-3d: Bridging the gap be- tween sparse points and shape-controllable text-to-3d gener- ation

Reference 34

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raw_fallback, observed 2026-08-07T12:33:39.579199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:37.856553Z digest=sha256:2b0da1193bf7f69be3620c9aa52830289a94f80891800b60625854a840577350

Observation 4be91d63-555f-43a0-afc8-64631d1e3a10 · outbound

This paper cites 3dilg: Ir- regular latent grids for 3d generative modeling.Advances in Neural Information Processing Systems, 35:21871–21885,.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework 3dilg: Ir- regular latent grids for 3d generative modeling.Advances in Neural Information Processing Systems, 35:21871–21885,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:33:39.406195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:37.943418Z digest=sha256:633e538aa8d1f1adf3f0be1ef46029e99fb52c99d12955ae49888f696d0b2bd9

Observation 765be0fa-94b9-4959-a1ab-aa2adb10b946 · outbound

This paper cites 3dshape2vecset: A 3d shape representation for neu- ral fields and generative diffusion models.ACM Transactions on Graphics (TOG), 42(4):1–16, 2023.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework 3dshape2vecset: A 3d shape representation for neu- ral fields and generative diffusion models.ACM Transactions on Graphics (TOG), 42(4):1–16, 2023

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:33:39.264135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:38.016442Z digest=sha256:4b45e732fd4157ed4c5a2ac5a648e964d7e824bb68364a8a787c96930f8a02cf

Observation 1adbf657-3076-4a16-bed6-286a73275ddb · outbound

This paper cites GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T12:33:38.104585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:38.104585Z digest=sha256:fd867580f83a41055b087967cdca10054da66808623d7171365bcd181a23a651

Observation d26a4439-31d4-496f-bb74-dd5e7854e083 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Adding conditional control to text-to-image diffusion models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T12:33:38.157033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:38.157033Z digest=sha256:41c0f17a1671e7f0850d1b35cd369ecf2e6256ebb8c8703df4c2ed86adf8a203

Observation 09731d65-093a-4de5-b92f-484bbfbb6a53 · outbound

This paper cites Clay: A controllable large-scale generative model for creat- ing high-quality 3d assets.ACM Transactions on Graphics (TOG), 43(4):1–20, 2024.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Clay: A controllable large-scale generative model for creat- ing high-quality 3d assets.ACM Transactions on Graphics (TOG), 43(4):1–20, 2024

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:33:39.091365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:38.230413Z digest=sha256:1959fe82b50422f0e9753d6f97000106e5d8e36b409aea236d028608f4772fc0

Observation f8ab4ab2-90ac-4046-96ae-78b2e6bd8874 · outbound

This paper cites Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation.Advances in Neural Information Processing Systems, 36, 2024.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation.Advances in Neural Information Processing Systems, 36, 2024

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:33:38.937115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:33:38.302030Z digest=sha256:e94f1f5f67325535671f8984b139d3b40df2548646aebfba80eb862061bae2db

Observation 861e2db1-8939-48a8-9724-d2f3b863eea2 · outbound

This paper cites HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance.

LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T12:33:38.368887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:33:38.368887Z digest=sha256:29bb48110b798741ffb6c4eb8b9f6e91ef70c384884cfc404fb4a4c9856798b4

Pith citing papers

No inbound Pith citation observations are available.