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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 2026-08-15T06:32:42.880941+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:c4412eae4c82ea77a6d06d7c4fe7cdbce312d6a28bbc9bc0d861909c8d43df20

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=sha256:58ec1b896f391b8195e07c8bcd1b4852a414127eb2c218a1678b16e717e5382a

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=sha256:012920f85cb52b41e0ed10a7744f241cae5c8a7cc635d7d6ba949e051a5f5c6a

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=sha256:5c039d48d0e288e1bf9443ff0578a630f2348886e605bb17b202d7ed03c5ccd6

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:a9bc08fbd1452194216885b87dbfa743f1eaafb0bc6bbf80c613c7d1f78aacc3

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:bcbb5f1bf123cb48e425bf093d0ff75bbfe60d5db0fc055fbd6fa17dbcb61098

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:dbdb522dc27ac3f360db7127f277555e0a491b2bf1c41ca5cc1dc5c37a709611

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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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:08.909154Z digest=sha256:402ef8f128c50c93fb28ebe7a1011c82d560228223a860981c61952ead9eebc1

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:08.913956Z digest=sha256:468a1fadc1287434aa52665e7c639b0cb062d306965d25b92b508aeaac90c269

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:08.918983Z digest=sha256:a44259d7f55d35ff10d1b02c74cc034e412b44ed707d3bf8d5238bb2f6eebeee

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=sha256:10e9003cb4b88fd554852456473b492e76c835da45f598d5e0e7dc9c37ed2bd8

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=sha256:7f7ca05347ec5bbc6d252b4476381ebd4ec4c0d51e8b4e4741922210188e59ac

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=sha256:7f7255fe715edb04d8f7391ce10d18c280daa3a7edc0d22981f2ee6bc2f8c2e2

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=sha256:8e365d058aa8d7f53d81483870ef2bd8a6d4ddfc443d760cd0b8e696e1a64dab

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:08.941969Z digest=sha256:891732799cf5302149f79d421b4d2d002b964324707a2f78481cea88b43cbee1

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:db55b9bcab602ba4c874ca56eb477845f81eee3cf91cd6dd43a75b24391cad8d

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=sha256:85bb69c54494cf40a8cb1ca2ccbdac9c6011292a2f32ecaac691464e70768b6d

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=sha256:060f30e756050587e0779c764ae75a95cc1fe886233bb11e360a7951e1fe3f86

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=sha256:7d0f28b57337ff12f8400338bfd9006d6327e0d3901195a1bd7773abf7ee5182

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:08.964619Z digest=sha256:c2d4105c77b9329e4644f0735397dc51b53462b33c9a64c0a154dcb8f56744de

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:08.969465Z digest=sha256:86ed70ef112959a3b0ef9a2897c5e308e7aac3ca377c5e8a163b94dca786d7f3

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:a0c95711ba6445a5b0cd04feec6d3df5a30fdd4e2db384c5cac5a2ec24b1cf48

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=sha256:6514931379d97210779b2a59c499b48695568dfa85185473ac9fc35bc9ab0170

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=sha256:0d311074e423d3aeed3bb183c029197a841602b22abb5610a9a1908f1b7d5c52

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:08.986890Z digest=sha256:2da6f32c526c6ad6e77d88606f5c797ceabd6bb7089c10d7e45d06d9bd3bcd36

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=sha256:7a9e784722807e4f45eeb676eee26e5a62961c21d00768f074206ea3b00e0e12

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=sha256:9848d46360a54b3667fd07324859144e7936f23915d04a43660d36e8197ba7ab

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=sha256:957a9ca80345d720f79cc70ac5e9b8117ae36f600eb25aea596715f43392b73c

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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source=pdf_text observed=2026-08-12T15:06:09.004704Z digest=sha256:f5b9b0cad5d08b62b930270a9645c1c5a2e28738bfe5b1b2185b3bfd50ad482a

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.008713Z digest=sha256:36839f2ba099aa997e109bedbf64e2cca1fe6bacb50fcb4285d9ce801de35f12

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:e82d02829dc81cb1f8d07ab972520287fc64908b4cb4f11007b0f86a8b34fd2d

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.017599Z digest=sha256:1744255491db963aa1056a3418af6dcbd01a8bc0aa747116ea95d42291faa72e

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.022255Z digest=sha256:2f9380cca50b3f14753e67826592e3c2125aca21583fa7eb42110c36e34bc1bb

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=sha256:24fcca0c47877b7438c6bbfd2050af68b8535e03826a2a41491bb6ed0b66470c

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.032414Z digest=sha256:95868df149a6821128764418e74b77095e59743bc072730fe9d241e0b3067250

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:cbd7e2666e9896dc32d59afbafebce5764993a213e2ebb3a4cbeed07e3e7ed49

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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source=pdf_text observed=2026-08-12T15:06:09.041883Z digest=sha256:bdb8640db2691f9c0686ad4b2e16fafc266813d26e5095cb14aef04a93986030

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.046471Z digest=sha256:73d7d33589c11f955e7ed390f0ddf5f632786d7956404b7d67210bd0ab3170c2

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
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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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.050887Z digest=sha256:a126647feb49d6e5063156d00f14d18ea1fcffaae3e8c996023267ae09499743

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=sha256:810a98dc0d223d97b80f951426afbd6dd0fc45ef46df0a147bc50ac86165986e

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.059549Z digest=sha256:3a9b0f898e676ce4109f7019d02f0e93461b807a87c97ccfb0710f88ea9e8e9a

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.064123Z digest=sha256:103f958aa2e828623fc037d7a258e920ec763cfe33ae5769db4f878507e7149f

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.068827Z digest=sha256:89345114519deb42c7ed2f0bf8bc88f39decdc749b939ad54d3205587593b689

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=sha256:65e9b6ce2dbd7bf03d2f1af8bd5eb9807b89deb05f8f989ce37b72a62323f78e

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.077126Z digest=sha256:bd3957ede583426900c1303e339378ff3a2883bc70c637c97873b78fb1dd7fa1

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=sha256:3e26992fac12e74e410649a4ada40ca76d342dee205320bf5dcc8bae78c88bd6

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=sha256:917fb0f2d9936bcfd40ed1e87b4691458afab6c808fb28259a51a965e5e4f177

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.091375Z digest=sha256:7c76136164689cd9b36c85d0450a5f77a7d732807316018bfdfe6246addadac0

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.095751Z digest=sha256:5dcd9f419e80e5f0c42c808c5c8ec3cf5d42f3d9aabfe63060fb73c0020942fd

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

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source=pdf_text observed=2026-08-12T15:06:09.100545Z digest=sha256:0a390116bd1a5d3ba2d574c192347a5638e12a6f4cad7180762efbbfbe32fb31

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:c8022852d60566942b2b37b0a773701acb434cd0d8cd9bc525f15cc0a54d35bc

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.109485Z digest=sha256:7ab59d07d35a7b4174561791afdc5946999c09f0b853d5ec7158a9e8bbf0d72a

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.113934Z digest=sha256:f53e42aee390e2fd2c72082cad5b8cb41820cf839759785959a1c85111a7f4e2

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.118401Z digest=sha256:c4a0c9e431e2cc9aae2a65ad3a6e3bd9ff012ca71a9051e36bebad9533c8c6c9

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:06:09.122829Z digest=sha256:c522e842ce6dad5ba3adf332669747084dadaffaab428485cf07c98318fb8f97

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.127052Z digest=sha256:6c2e7f6ae5da0b566cc853bb6d592c34c29fb877fb3d4e89bd90dde5b265e1f7

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=sha256:5beecf34670983d6cab90bc1003228ec785ed7bc25eef7b2235084a9e0e582de

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=sha256:7d50dee04da89667d01b71cc7b4cc13331d7c73e35dc4835825b5f88e7d15c7c

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=sha256:36851dc311fd7ca58d4e65595fc48bd132bffdb2e762fcc21374845dd67536bd

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.145453Z digest=sha256:9478d54c913b327af84667895bbbd1a793bcb3c374427df3ec45aadc93565c75

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.150092Z digest=sha256:01e99dccba58a9a44d2f96d64eb51fc54d64e82520861db60fd4366742b5e8db

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=sha256:348933d772309229aaa1c15dc116db5fd3cb895d3e0e7cd3188938b53f9800e2

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.159386Z digest=sha256:e40ceb08ae45f10d30a56cb0a9d3470809e9c57254d088548395d7699f25222e

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.164581Z digest=sha256:b20104287b7e38fb4150e57748cb42a6f34246b8b7b9adb9d17f132c3678c246

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.169023Z digest=sha256:a89784645d360e6f2aaa1778634975e412b6a09d8ed5e1a0c4e97d41eec02345

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.173948Z digest=sha256:64c79451402fab76c89f68dec695467536bad974a68e08c9e9f29f8291962dab

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
unresolved
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:f04b016bdf22313cb4b3bffbb338499969ed1d45036640eb507b4e6d416190df

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=sha256:49d1cb5b4160c7e0fcd459b5dcffbcdbba2f7c5341cd634a7effd18ce0ee4008

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:fe97b557ae55654f619b6d1f6cf1b5e274da7cf42a8969937030390091d2e1b9

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=sha256:9a142051d4129b74f779f7a55c7700d5fc9e9ec1b06f1c5405374f368e97d254

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
unresolved
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=sha256:52dc6a876773ea98587894967ec216e75961b4d65badb943e168a34bd572f0c8

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:dc16f34fcca7f779cc93089b464fe470fbe9a923c270db72133dda333c5811f2

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T15:06:09.205961Z digest=sha256:84785c7c2ad286316f5b95af62538a2bab37e6882175448d5e94c35cf4c8811e

Pith citing papers

No inbound Pith citation observations are available.