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Paper Citation Record · LEDGER

Any-to-3D Generation via Hybrid Diffusion Supervision

As of 15 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 0 inbound Pith citation observations for arXiv:2411.14715.

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

pith.paper-citation-record.v1
2411.14715 v1

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:06:09.205961Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

74 of 74 outbound references displayed

  • verified exact1
  • verified fuzzy29
  • unresolved44
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 58a48177-60ee-4fdc-a9b5-725dc1b10bd4 · outbound

This paper cites A Comprehensive Survey on 3D Content Generation.

Any-to-3D Generation via Hybrid Diffusion Supervision A Comprehensive Survey on 3D Content Generation

Reference 1

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Observation ab03ce1c-0c6e-4f5b-9e17-ecf0049af528 · outbound

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

Any-to-3D Generation via Hybrid Diffusion Supervision MVDream: Multi-view Diffusion for 3D Generation

Reference 2

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source=pdf_text observed=2026-08-12T15:06:08.874821Z digest=sha256:69ac3358477798b638a127e78b7bc546c84765537c8d365db60756cc6c9c2137

Observation 53913581-a70d-492d-ba92-411094513296 · outbound

This paper cites DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior.

Any-to-3D Generation via Hybrid Diffusion Supervision DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior

Reference 3

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source=pdf_text observed=2026-08-12T15:06:08.879990Z digest=sha256:c9ab207c3effe18a06fc0c8ae5eb2fec89a7b01fe2d3ac8e35f81d29d761747e

Observation 12200ea4-2b8b-4f1c-a655-ef1cb38aeb84 · outbound

This paper cites CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model.

Any-to-3D Generation via Hybrid Diffusion Supervision CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model

Reference 4

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source=pdf_text observed=2026-08-12T15:06:08.885295Z digest=sha256:823c25eb89acb306c37d875eb2314b239dc476cb5c0336778fee44da32e0c359

Observation 17bbf6d2-9bdd-4f05-a353-e177767ba10c · outbound

This paper cites Scalable diffusion models with transformers,.

Any-to-3D Generation via Hybrid Diffusion Supervision Scalable diffusion models with transformers,

Reference 5

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source=pdf_text observed=2026-08-12T15:06:08.890661Z digest=sha256:abca6ab4838b9123a4b9e892c49185d77cd44a95f9cda5bf917e2051fd452ffd

Observation 61fc271f-8f2d-437c-b16b-4651d3bb08b3 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Any-to-3D Generation via Hybrid Diffusion Supervision U-net: Convolutional networks for biomedical image segmentation,

Reference 6

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source=pdf_text observed=2026-08-12T15:06:08.895370Z digest=sha256:0b853e25dffd39b3e50374f66df198f2559505b50349d195313e268e63448b65

Observation 620e5ff1-740d-4c29-9519-64e408ceb3cd · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Any-to-3D Generation via Hybrid Diffusion Supervision Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 7

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source=pdf_text observed=2026-08-12T15:06:08.900106Z digest=sha256:de1ea7529471ce20008ffd2744f37daa82c989c03c7b1014bd38f183ac39d6cf

Observation f76566cb-5053-4abd-a366-4928b4792962 · outbound

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

Any-to-3D Generation via Hybrid Diffusion Supervision High- resolution image synthesis with latent diffusion models,

Reference 8

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source=pdf_text observed=2026-08-12T15:06:08.904651Z digest=sha256:6232abe7cd5570b1e4d01e5cfae9a9cd5d11553a065673bc9db93f89b5a14d15

Observation f97915fa-8ab2-4c8c-9415-1a6793b0dd37 · outbound

This paper cites Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation,.

Any-to-3D Generation via Hybrid Diffusion Supervision Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:08.909154Z digest=sha256:3f885fc3851051e0721d325a02353e5c513fe543b95c0136f4eb5e3a467a41ae

Observation 4b764b39-6668-41aa-8e23-6dc37b695d8c · outbound

This paper cites Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation,.

Any-to-3D Generation via Hybrid Diffusion Supervision Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation,

Reference 10

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:08.913956Z digest=sha256:b8bc7112fe05e88c0c0a2592f10b870fed015cf14dff905e4aa43bde6a028c01

Observation b4f8d8df-8334-450c-98b3-f36758c75bda · outbound

This paper cites Consistent3d: Towards consistent high-fidelity text-to-3d generation with deterministic sampling prior,.

Any-to-3D Generation via Hybrid Diffusion Supervision Consistent3d: Towards consistent high-fidelity text-to-3d generation with deterministic sampling prior,

Reference 11

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:08.918983Z digest=sha256:a8704ce38839fc5b0fc8734c13c1980ddc49a2f74f875ea8908e74d4e18202b7

Observation 0795ee22-0282-4593-956c-5be708aa389c · outbound

This paper cites Score distillation sampling with learned manifold corrective,.

Any-to-3D Generation via Hybrid Diffusion Supervision Score distillation sampling with learned manifold corrective,

Reference 12

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source=pdf_text observed=2026-08-12T15:06:08.923377Z digest=sha256:cacb149d313e94641dfffef9e6f921777ce663fb403f0c22ab60e8199f9af5a6

Observation ccef9134-5c18-47b1-a5ba-466e9707076e · outbound

This paper cites Taming Mode Collapse in Score Distillation for Text-to-3D Generation.

Any-to-3D Generation via Hybrid Diffusion Supervision Taming Mode Collapse in Score Distillation for Text-to-3D Generation

Reference 13

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source=pdf_text observed=2026-08-12T15:06:08.927827Z digest=sha256:55921ace9e87f29c334b644bfddce0e068d9a79403433b2127be7dcddf0ec04f

Observation 69cf0a3c-c85f-4c2a-aff1-6f56c6d6075b · outbound

This paper cites Prolific- dreamer: High-fidelity and diverse text-to-3d generation with variational score distillation,.

Any-to-3D Generation via Hybrid Diffusion Supervision Prolific- dreamer: High-fidelity and diverse text-to-3d generation with variational score distillation,

Reference 14

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source=pdf_text observed=2026-08-12T15:06:08.932799Z digest=sha256:d67eca6a79cd34b947577fcbad27f7bfa25515fa1df91a599e02a256f4108fe6

Observation f8e4a336-e408-42c7-81ad-1da07bd3d4e9 · outbound

This paper cites Text2mesh: Text-driven neural stylization for meshes,.

Any-to-3D Generation via Hybrid Diffusion Supervision Text2mesh: Text-driven neural stylization for meshes,

Reference 15

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source=pdf_text observed=2026-08-12T15:06:08.937467Z digest=sha256:07fac86b9533299b8dca48fd959332f8a00eab3306e20c73e372cbdaf3aad6ea

Observation 325a509d-8d1c-4246-9f42-31754ffbae7b · outbound

This paper cites Cad: Photorealistic 3d generation via adversarial dis- tillation,.

Any-to-3D Generation via Hybrid Diffusion Supervision Cad: Photorealistic 3d generation via adversarial dis- tillation,

Reference 16

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:08.941969Z digest=sha256:f55dfa18a7eab0a78c431d8a8ac5ff681ddd597c9920926d61a6beb948ba8e01

Observation 25272064-4dff-4f97-8c82-69108bc4d387 · outbound

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

Any-to-3D Generation via Hybrid Diffusion Supervision DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation

Reference 17

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source=pdf_text observed=2026-08-12T15:06:08.946191Z digest=sha256:5ee255f4e6d79790de64edb477d227eb4c04f62cd65584a66f712f459d00d7fa

Observation c7f2fbb1-06e5-4912-8725-19b4e7dafbb6 · outbound

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

Any-to-3D Generation via Hybrid Diffusion Supervision Make-it-3d: High-fidelity 3d creation from a single image with diffu- sion prior,

Reference 18

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source=pdf_text observed=2026-08-12T15:06:08.950956Z digest=sha256:4676761b98d8300ed1807720fc173218a0832d3f4c0748688086bb9ddcb3a3cb

Observation b2bb9bbb-7f2b-4f0e-b993-bdd9af1513a1 · outbound

This paper cites Realfusion: 360deg reconstruction of any object from a single image,.

Any-to-3D Generation via Hybrid Diffusion Supervision Realfusion: 360deg reconstruction of any object from a single image,

Reference 19

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source=pdf_text observed=2026-08-12T15:06:08.955796Z digest=sha256:0191cfa4deaa4136f0b6cd81a98b1ba6cb12752a87db1a0494fd756ec2d41bff

Observation 5fdb51db-88ad-4a3e-bb6e-2ea921db3796 · outbound

This paper cites Imagebind: One embedding space to bind them all,.

Any-to-3D Generation via Hybrid Diffusion Supervision Imagebind: One embedding space to bind them all,

Reference 20

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source=pdf_text observed=2026-08-12T15:06:08.960132Z digest=sha256:a68a2d9f14ecf23d8600485bdede029d2a9e785b550a7280aa7e3af1de77bf54

Observation e3e7c479-c0d2-4dae-8828-aafe5bf81840 · outbound

This paper cites Any-to-any generation via composable diffusion,.

Any-to-3D Generation via Hybrid Diffusion Supervision Any-to-any generation via composable diffusion,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:08.964619Z digest=sha256:6b951dcd8172d61648347d46b4a5fb52af0df713158cba9f8ad35825eb8287bd

Observation c8402f07-2dc1-4427-b397-75901738a4e1 · outbound

This paper cites Dreamfusion: Text- to-3d using 2d diffusion,.

Any-to-3D Generation via Hybrid Diffusion Supervision Dreamfusion: Text- to-3d using 2d diffusion,

Reference 22

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:08.969465Z digest=sha256:5478a399e163342093eeccdad9f8009f56df17de2b3c0dd9dd6089ed503fdab5

Observation f1a2d67b-b25a-4a63-90e5-9a02c3543115 · outbound

This paper cites Zero-1-to-3: Zero-shot one image to 3d object,.

Any-to-3D Generation via Hybrid Diffusion Supervision Zero-1-to-3: Zero-shot one image to 3d object,

Reference 23

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source=pdf_text observed=2026-08-12T15:06:08.973776Z digest=sha256:c08c66d1e8e45802eca2da498e0de572490bfcd3fd5719e4649fefd1329d4410

Observation 8dad3f68-d79e-478a-9369-ab314585df38 · outbound

This paper cites Mip-nerf: A multiscale representation for anti- aliasing neural radiance fields,.

Any-to-3D Generation via Hybrid Diffusion Supervision Mip-nerf: A multiscale representation for anti- aliasing neural radiance fields,

Reference 24

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source=pdf_text observed=2026-08-12T15:06:08.978522Z digest=sha256:b6b0e53239a21f3810814542866fed4cb95b0853f4b4148b8535435b60920dd1

Observation 1ea507c1-f81b-471a-962c-4a8e3753c7ef · outbound

This paper cites Instant neural graphics primitives with a multiresolution hash encoding,.

Any-to-3D Generation via Hybrid Diffusion Supervision Instant neural graphics primitives with a multiresolution hash encoding,

Reference 25

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source=pdf_text observed=2026-08-12T15:06:08.982694Z digest=sha256:f1046e6c4f8aef1308ce3dd121ac9aaebf5dc58792c938dbf79dc330d24727aa

Observation 8edee7e6-22fa-4a4a-b252-dad2923d0e82 · outbound

This paper cites Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthe- sis,.

Any-to-3D Generation via Hybrid Diffusion Supervision Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthe- sis,

Reference 26

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:08.986890Z digest=sha256:24882df76a081972f9bcb9d652ca29d00aec03820d1264c0fb1fe098b2e200c5

Observation f7b5b759-ce9c-40ce-a036-366ed4553002 · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applications,.

Any-to-3D Generation via Hybrid Diffusion Supervision Diffusion models: A comprehensive survey of methods and applications,

Reference 27

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source=pdf_text observed=2026-08-12T15:06:08.991441Z digest=sha256:1077ddf94d6669d8143ad3de4f7890b8ae8f5d5c37872e189ebe8864d36996f7

Observation 26f0b8c1-cdb7-4c16-8e8d-f4470e745cb3 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Any-to-3D Generation via Hybrid Diffusion Supervision Score-Based Generative Modeling through Stochastic Differential Equations

Reference 28

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source=pdf_text observed=2026-08-12T15:06:08.995743Z digest=sha256:9ea8a79f76a979e51128d34d7ce6b2b0c61152c3a55ed4dea37d4d1464a15669

Observation d15f644d-4a79-416d-8021-b2b826610ef0 · outbound

This paper cites Denoising diffusion probabilistic models,.

Any-to-3D Generation via Hybrid Diffusion Supervision Denoising diffusion probabilistic models,

Reference 29

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source=pdf_text observed=2026-08-12T15:06:09.000129Z digest=sha256:b3e4c52038ee483b7c7a4b8cc9c48240bb4ffb2198e9aa42ea584745a3457753

Observation c1627c15-bda4-46ff-af79-dc587a4ce8b7 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Any-to-3D Generation via Hybrid Diffusion Supervision Learning transferable visual models from natural language supervision,

Reference 30

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Observation c752e858-f635-414a-8e67-cda7edfef26b · outbound

This paper cites Vector quantized diffusion model for text-to-image synthesis,.

Any-to-3D Generation via Hybrid Diffusion Supervision Vector quantized diffusion model for text-to-image synthesis,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.008713Z digest=sha256:099bc42ec0893162f78e28989e718bec699b9d8b5965fee9f716fceb7e2db662

Observation 7ea2d145-5880-468b-87c6-958f42bd2fde · outbound

This paper cites Diffusion models beat gans on image synthesis,.

Any-to-3D Generation via Hybrid Diffusion Supervision Diffusion models beat gans on image synthesis,

Reference 32

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source=pdf_text observed=2026-08-12T15:06:09.013153Z digest=sha256:df50f3f3b05024f8f3c2893507be96764cf3661cc550220e133340d78260dc31

Observation b60c36b8-a853-4783-861a-7a0944dabc67 · outbound

This paper cites Improving diffusion-based image synthesis with context pre- diction,.

Any-to-3D Generation via Hybrid Diffusion Supervision Improving diffusion-based image synthesis with context pre- diction,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.017599Z digest=sha256:d6c606ee7f034b73c62745bce47711d5b589977715e133bfa487fc6809c34dbe

Observation a0d02342-8316-442f-8a2d-ba0a69d5d526 · outbound

This paper cites Shifted diffusion for text-to-image generation,.

Any-to-3D Generation via Hybrid Diffusion Supervision Shifted diffusion for text-to-image generation,

Reference 34

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.022255Z digest=sha256:eb7a58c9a7237abe5016a5f4e3835ca880dc962874b590ca53dc78f848b2bd4c

Observation 91f627df-ae0e-4541-a448-a3415e41534e · outbound

This paper cites Text2video-zero: Text-to-image diffusion models are zero-shot video generators,.

Any-to-3D Generation via Hybrid Diffusion Supervision Text2video-zero: Text-to-image diffusion models are zero-shot video generators,

Reference 35

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source=pdf_text observed=2026-08-12T15:06:09.027051Z digest=sha256:506f02feefdab2dfb89e6430ee78fbf21995ac81e7db4f219ede4f2162e0eb47

Observation 76e529bc-9e94-47fe-9c0b-8ded0e7a27de · outbound

This paper cites Videofusion: Decomposed diffusion models for high-quality video generation,.

Any-to-3D Generation via Hybrid Diffusion Supervision Videofusion: Decomposed diffusion models for high-quality video generation,

Reference 36

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raw_fallback, observed 2026-08-12T15:06:09.893071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.032414Z digest=sha256:a0da09062fff8441bedcf391370338a8e324d587acb89e4df040a3fc13098621

Observation f62fb30d-c7dc-42a7-be9b-2f2feddca30b · outbound

This paper cites Vidm: Video implicit diffusion models,.

Any-to-3D Generation via Hybrid Diffusion Supervision Vidm: Video implicit diffusion models,

Reference 37

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.037210Z digest=sha256:50abd88b41acb06d5f6054b68cc572700f7894e583847d5386902bdb9f377df3

Observation 402b17df-3908-41c7-a353-1bbe49809c2c · outbound

This paper cites LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models.

Any-to-3D Generation via Hybrid Diffusion Supervision LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models

Reference 38

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.041883Z digest=sha256:0fda522aec7856d36db8dc2e184611fafc6fcc5176ed94ee69d83862b5ab16d5

Observation cbc742d2-1eef-4fd2-b7d4-148dec3d0f0a · outbound

This paper cites Prodiff: Progressive fast diffusion model for high-quality text-to-speech,.

Any-to-3D Generation via Hybrid Diffusion Supervision Prodiff: Progressive fast diffusion model for high-quality text-to-speech,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.867485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.046471Z digest=sha256:5b957605aa9f9439f227ed39188e11eca1c1bec57cad724492815369761cd8e5

Observation f53e03e5-aef3-4931-98b6-8cb45fd5232d · outbound

This paper cites Taming diffusion models for audio-driven co-speech gesture generation,.

Any-to-3D Generation via Hybrid Diffusion Supervision Taming diffusion models for audio-driven co-speech gesture generation,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.851715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.050887Z digest=sha256:5c5bc2cbb6ae23b492abef3ee1e2b29b2bf89d5c5eb32e485369c1339a66da92

Observation 1c8bda65-74c7-4d6c-b79e-174173688c26 · outbound

This paper cites Speech enhancement and dereverberation with diffusion-based genera- tive models,.

Any-to-3D Generation via Hybrid Diffusion Supervision Speech enhancement and dereverberation with diffusion-based genera- tive models,

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.055185Z digest=sha256:ebf985f271c58368db567e81bb00ced54e5cd9cfd9f5b1052bf09366e12a7be2

Observation c1009c3e-ef11-4e4d-a58d-d449c68cef03 · outbound

This paper cites Storm: A diffusion-based stochastic regeneration model for speech enhancement and dereverberation,.

Any-to-3D Generation via Hybrid Diffusion Supervision Storm: A diffusion-based stochastic regeneration model for speech enhancement and dereverberation,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.826556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.059549Z digest=sha256:275fca5d2581ad36f1178549a336e79b67798b18178aa8f8e7e376105f8c23e4

Observation ffa2b3a8-f75f-4826-80b1-5a5436ff3713 · outbound

This paper cites HyperFields: Towards Zero-Shot Generation of NeRFs from Text.

Any-to-3D Generation via Hybrid Diffusion Supervision HyperFields: Towards Zero-Shot Generation of NeRFs from Text

Reference 43

Resolution
verified exact
local_arxiv, observed 2026-08-12T15:06:09.369297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.064123Z digest=sha256:a6bc8ccb8761ea43c3f66bceab322c18a70da98daab5c71a3aeae8e1ff632552

Observation 4046219f-73da-4020-8c5c-9b4648149132 · outbound

This paper cites Texfusion: Synthesiz- ing 3d textures with text-guided image diffusion models,.

Any-to-3D Generation via Hybrid Diffusion Supervision Texfusion: Synthesiz- ing 3d textures with text-guided image diffusion models,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.810996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.068827Z digest=sha256:6d86934d145522522398440a33b5433399cf7d462b71f474b6cfb87ec6d02ac5

Observation 96ec87b7-777f-414e-8707-6f16341013e7 · outbound

This paper cites Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching,.

Any-to-3D Generation via Hybrid Diffusion Supervision Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching,

Reference 45

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.072978Z digest=sha256:364116bf6949f63141875771fa3370e98886021a34bb761219e2d7aa26c44b89

Observation c969aad8-aa71-4917-aaf1-439b7ece00dc · outbound

This paper cites Scenetex: High-quality texture synthesis for indoor scenes via diffusion priors,.

Any-to-3D Generation via Hybrid Diffusion Supervision Scenetex: High-quality texture synthesis for indoor scenes via diffusion priors,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.785723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.077126Z digest=sha256:8c3fed6f0c069154d01c13bdc0a25bd3c2707a7938dd7ea5b29b55310d32224d

Observation 152a28b7-d752-4fc1-9678-ea58cfdeb0b9 · outbound

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

Any-to-3D Generation via Hybrid Diffusion Supervision High- resolution image synthesis with latent diffusion models,

Reference 47

Resolution
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no resolver link, observed 2026-08-12T15:06:09.081939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.081939Z digest=sha256:891aaff43ab7ca0173e40783e108a72bf4314543d0999374a7a35313dfa9b63d

Observation 9c6ebb7f-ce0b-4c56-aee1-5138a6bc3ecd · outbound

This paper cites SyncDreamer: Generating Multiview-consistent Images from a Single-view Image.

Any-to-3D Generation via Hybrid Diffusion Supervision SyncDreamer: Generating Multiview-consistent Images from a Single-view Image

Reference 48

Resolution
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no resolver link, observed 2026-08-12T15:06:09.086742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.086742Z digest=sha256:2b6071812163ba13d0e7923bd835f9f521f5ae5b5d881680fef9d4b748c0202d

Observation a1758340-b61d-41f2-8a1f-7a3ab483c63f · outbound

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

Any-to-3D Generation via Hybrid Diffusion Supervision Objaverse: A universe of annotated 3d objects,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.760556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.091375Z digest=sha256:2a394905b297bc655acf9c95111cfb6dfdcbcf0b57694b407b6ff8bb515c30f8

Observation 326f62a7-0b69-4885-9f52-31750f438126 · outbound

This paper cites Objaverse-xl: A uni- verse of 10m+ 3d objects,.

Any-to-3D Generation via Hybrid Diffusion Supervision Objaverse-xl: A uni- verse of 10m+ 3d objects,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.745915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.095751Z digest=sha256:519103788701c006af35529411de2cc55aa723b4a3343032b14bdde00b75c5f6

Observation c960da25-e218-4a06-b48b-62718270d6cc · outbound

This paper cites Omnivore: A single model for many visual modalities,.

Any-to-3D Generation via Hybrid Diffusion Supervision Omnivore: A single model for many visual modalities,

Reference 51

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.100545Z digest=sha256:52a1873023af6f2af059f4ca0f58cf758ca15ddc3111726cbb25f83d1a7fbae6

Observation 2ca5aaf4-4b5a-41ac-b0b2-5f6cc8611c7c · outbound

This paper cites PolyViT: Co-training Vision Transformers on Images, Videos and Audio.

Any-to-3D Generation via Hybrid Diffusion Supervision PolyViT: Co-training Vision Transformers on Images, Videos and Audio

Reference 52

Resolution
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no resolver link, observed 2026-08-12T15:06:09.105043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.105043Z digest=sha256:a6b5ced3ec7a269e5face8501311235f7038cd48636ae3889af7ac1b62d6d6a4

Observation d455fe45-28c2-4493-80df-62d3e04ce503 · outbound

This paper cites Look, listen and learn,.

Any-to-3D Generation via Hybrid Diffusion Supervision Look, listen and learn,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.721471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.109485Z digest=sha256:69829926d5ef641f9c66eb9411ed0494e106b70f49472d05eb08fcc15a41a5f1

Observation 66dd7062-c45f-4416-992a-93c83159a1cc · outbound

This paper cites Omnimae: Single model masked pretraining on images and videos,.

Any-to-3D Generation via Hybrid Diffusion Supervision Omnimae: Single model masked pretraining on images and videos,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.707135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.113934Z digest=sha256:6eaffb8a784cbb9aa02b4f6a85ddeeb4f138a06864e9a2a17b107141fc927fc1

Observation c2ff91f4-95fa-48f7-83a4-1192399cb610 · outbound

This paper cites Audio-visual instance discrimination with cross-modal agreement,.

Any-to-3D Generation via Hybrid Diffusion Supervision Audio-visual instance discrimination with cross-modal agreement,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.692449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.118401Z digest=sha256:40748046b9b434a4cf87f6a439ca82ceb535c22390e7560eb7695d3f8eb702f9

Observation f68eb4b9-c82a-4258-a31b-a1e0edbbc09b · outbound

This paper cites Contrastive multiview coding,.

Any-to-3D Generation via Hybrid Diffusion Supervision Contrastive multiview coding,

Reference 56

Resolution
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no resolver link, observed 2026-08-12T15:06:09.122829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.122829Z digest=sha256:1a6a23b5c534ccf6a621d9191a00afd02f8d8429774f0bc8a09194fef5a2a254

Observation 9597dfb0-6242-4426-af2a-9c4b64904947 · outbound

This paper cites Bevt: Bert pretraining of video transformers,.

Any-to-3D Generation via Hybrid Diffusion Supervision Bevt: Bert pretraining of video transformers,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.667448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.127052Z digest=sha256:4c4af741e0f2814c93319898e8525e1b1f6e566879ebb5769184e28020594b5d

Observation f8a5fd88-b8d2-4d96-b629-78a21a56a2e9 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Any-to-3D Generation via Hybrid Diffusion Supervision An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 58

Resolution
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no resolver link, observed 2026-08-12T15:06:09.131536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.131536Z digest=sha256:caa9ad4ff3f38465c29e9adf684bf982da7596449d9c3630a0ae3fd435dd8fd9

Observation ccd53418-b010-4471-988f-95f55e04344f · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

Any-to-3D Generation via Hybrid Diffusion Supervision Flamingo: a visual language model for few-shot learning,

Reference 59

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.136242Z digest=sha256:626cc08cb4a8945e9b01da3c30d42c415d1a5c45ee680c14051c6ebcbe0478f1

Observation e313d8b7-e60b-4394-84aa-ce78ae8bd06c · outbound

This paper cites Unifying vision-and-language tasks via text generation,.

Any-to-3D Generation via Hybrid Diffusion Supervision Unifying vision-and-language tasks via text generation,

Reference 60

Resolution
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no resolver link, observed 2026-08-12T15:06:09.140586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.140586Z digest=sha256:68ef29711e2565cc575be2329e282bfb745020c3d6d4234f41add0491475762f

Observation cbc33707-dc53-4f18-9fd3-00853989f456 · outbound

This paper cites Merlot: Multimodal neural script knowledge models,.

Any-to-3D Generation via Hybrid Diffusion Supervision Merlot: Multimodal neural script knowledge models,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.630843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.145453Z digest=sha256:d7182669cc451906663ab3c864848f3ca3208e81966a62a901a7fbb9c5c3d03e

Observation 7a20f2a2-126e-4a0b-b512-feebae9509d2 · outbound

This paper cites Multimodal few-shot learning with frozen language models,.

Any-to-3D Generation via Hybrid Diffusion Supervision Multimodal few-shot learning with frozen language models,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.613813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.150092Z digest=sha256:d1b1f41ebce70e67a1553d6cbe57bd6acd8d044b2310b7f29b819607f8dec1d8

Observation d1262267-e2dc-430e-81d1-5e465e451c67 · outbound

This paper cites Enhancing Visual Grounding and Generalization: A Multi-Task Cycle Training Approach for Vision-Language Models.

Any-to-3D Generation via Hybrid Diffusion Supervision Enhancing Visual Grounding and Generalization: A Multi-Task Cycle Training Approach for Vision-Language Models

Reference 63

Resolution
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no resolver link, observed 2026-08-12T15:06:09.154447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.154447Z digest=sha256:3e5f4775a2890d862ee21f942e3e81c70f3c4409649a2ebef333714eee408d27

Observation 91929523-abef-4ee5-bd5f-f24e179c6f8b · outbound

This paper cites Tvlt: Textless vision- language transformer,.

Any-to-3D Generation via Hybrid Diffusion Supervision Tvlt: Textless vision- language transformer,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.598574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.159386Z digest=sha256:a7423633fe52793cb3bf3130b7606cbc5f86fdafc17f9b0e40629bac0ac24ab7

Observation 24568667-8c93-4b8e-ae8e-e39e705dc7ad · outbound

This paper cites i-code: An integrative and composable multimodal learning framework,.

Any-to-3D Generation via Hybrid Diffusion Supervision i-code: An integrative and composable multimodal learning framework,

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.583209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.164581Z digest=sha256:6e535805b8833b57117a898e6a691d9432d74641dba0a8986b955ff86935e86e

Observation bed8ef28-4106-4293-8a4c-2788d6ce3475 · outbound

This paper cites Merlot reserve: Neural script knowledge through vision and language and sound,.

Any-to-3D Generation via Hybrid Diffusion Supervision Merlot reserve: Neural script knowledge through vision and language and sound,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.566661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.169023Z digest=sha256:e118bf66085671485d82e39c9f641edeaa92b4b2d5e2dab3030d26a491b82a1f

Observation 278ba3f8-a417-4148-ba47-941a6f9cfa78 · outbound

This paper cites Codi-2: In-context interleaved and interactive any-to-any generation,.

Any-to-3D Generation via Hybrid Diffusion Supervision Codi-2: In-context interleaved and interactive any-to-any generation,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.551448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.173948Z digest=sha256:faa76eac9f48f3cb44e1a60f6bc549881001a2ca3516d3c57a004e1c150bb281

Observation 0d7bda40-a9b7-4578-9fab-bd0def96b275 · outbound

This paper cites Clap learning audio concepts from natural language supervision,.

Any-to-3D Generation via Hybrid Diffusion Supervision Clap learning audio concepts from natural language supervision,

Reference 68

Resolution
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no resolver link, observed 2026-08-12T15:06:09.178506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.178506Z digest=sha256:439909bd7128a7ef8bd0b0ed0def413a30f3fb06172235cdc0ebc17a1375bb82

Observation 14becb8c-e340-404d-b335-eae95a2b7a86 · outbound

This paper cites AudioLDM: Text-to-Audio Generation with Latent Diffusion Models.

Any-to-3D Generation via Hybrid Diffusion Supervision AudioLDM: Text-to-Audio Generation with Latent Diffusion Models

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-12T15:06:09.182850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.182850Z digest=sha256:d9267307938fc8067f1187819e0ff45e49b3ecbd73d441970c61020f5cbac051

Observation 165f4ef6-ebb0-441c-b50c-e4cf30ccecd4 · outbound

This paper cites ClipCap: CLIP Prefix for Image Captioning.

Any-to-3D Generation via Hybrid Diffusion Supervision ClipCap: CLIP Prefix for Image Captioning

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-12T15:06:09.187306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.187306Z digest=sha256:b93d5c2937e6cbd0f06b0e2b5bff721773b3d62f345bf9870900af3208c19c13

Observation ffde865b-5216-4021-805b-079c3d710f1d · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Any-to-3D Generation via Hybrid Diffusion Supervision Representation Learning with Contrastive Predictive Coding

Reference 71

Resolution
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no resolver link, observed 2026-08-12T15:06:09.192123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.192123Z digest=sha256:f16e33926a8d48ca8d43694515cc8df7c18e165274d0ea3571405309f096b089

Observation 32346f7b-7d32-4e2c-bbde-806ea81e4236 · outbound

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

Any-to-3D Generation via Hybrid Diffusion Supervision HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance

Reference 72

Resolution
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no resolver link, observed 2026-08-12T15:06:09.196597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.196597Z digest=sha256:e45bcfdae779e5667ea790e8cdcaebd76b4d5fd0fa921f5512bcb7b129d0e830

Observation 93f1e07f-3939-4189-b9d5-5a20c346064f · outbound

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

Any-to-3D Generation via Hybrid Diffusion Supervision Magic3d: High-resolution text-to- 3d content creation,

Reference 73

Resolution
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no resolver link, observed 2026-08-12T15:06:09.201401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.201401Z digest=sha256:b72864700bfbf94b593b920fbb45a63d21e05ce1072c6d78af32d80a03aebf01

Observation b6c33e85-a032-46af-9e58-f116ec26e3f9 · outbound

This paper cites threestudio: A unified framework for 3d content generation,.

Any-to-3D Generation via Hybrid Diffusion Supervision threestudio: A unified framework for 3d content generation,

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:06:09.518344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:06:09.205961Z digest=sha256:f1644bb3aef8fe2c089845ccf4438fe6b2ab7f25f95a1f0b84aaa8844a15120b

Pith citing papers

No inbound Pith citation observations are available.