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Paper Citation Record · LEDGER

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation

As of 8 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2508.06392.

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

pith.paper-citation-record.v1
2508.06392 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:49:11.657782Z

measured 54 of 54 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

54 of 54 outbound references displayed

  • verified exact2
  • verified fuzzy21
  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ccbefc12-baf3-4994-85dd-da9dc30f34bf · outbound

This paper cites write newline.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation write newline

Reference 1

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-05T22:49:06.375982Z digest=sha256:e97e2fd62ac793ec6e64caf90825af51c0d87575d014d8cd246de43509f475d5

Observation f8e4a67e-ff8b-4a44-aaa1-07effb6222d9 · outbound

This paper cites Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields

Reference 2

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raw_fallback, observed 2026-08-05T22:49:16.482340Z

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=arxiv_source observed=2026-08-05T22:49:06.452039Z digest=sha256:38ef216bdc6b66fa1e1b6e1f45b86396c79f217cc0060b287bccdf4c8261e728

Observation da0ef3b4-d0e8-4554-a0c7-0ed46e68d865 · outbound

This paper cites Align your latents: High-resolution video synthesis with latent diffusion models.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Align your latents: High-resolution video synthesis with latent diffusion models

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-05T22:49:16.184070Z

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=arxiv_source observed=2026-08-05T22:49:06.559956Z digest=sha256:323395aa4766abc68da6bb62c440ae9ff6d41c5ab405a5062ff8831828cfbcac

Observation c990fbd8-7836-4c68-86f9-0fa3e47c4d35 · outbound

This paper cites NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training

Reference 4

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local_arxiv, observed 2026-08-05T22:49:12.641962Z

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=arxiv_source observed=2026-08-05T22:49:06.669129Z digest=sha256:ccb849b258d0eac24c97a5ec1056d389aaa6f4056606cea7a3c5cd33c9681177

Observation 1825d15d-e90d-4498-a70d-1534570fb955 · outbound

This paper cites Videocrafter2: Overcoming data limitations for high-quality video diffusion models.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Videocrafter2: Overcoming data limitations for high-quality video diffusion models

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-05T22:49:15.914583Z

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=arxiv_source observed=2026-08-05T22:49:06.796738Z digest=sha256:e416c97861c5f995aced0dd55dc560d40173b19e6b37c59557e5fd3f121a15f7

Observation c87ce0de-6281-461c-9c3f-7dcf1f98e93a · outbound

This paper cites MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images

Reference 6

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source=arxiv_source observed=2026-08-05T22:49:06.897267Z digest=sha256:9c28f714d008ccd3c22906ba386dc1b42887203149ac6d1d22e157f7c9de3c36

Observation 4d90b587-b603-46ed-9c88-0ac3f5673a54 · outbound

This paper cites Mvsplat360: Feed-forward 360 scene synthesis from sparse views.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Mvsplat360: Feed-forward 360 scene synthesis from sparse views

Reference 7

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raw_fallback, observed 2026-08-05T22:49:15.551785Z

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=arxiv_source observed=2026-08-05T22:49:06.977488Z digest=sha256:f4756ecedd5d1271e80a1907091c33a8b58763ae031a0ac9da014e41f8595f0d

Observation 5115e967-07b9-4c6f-866e-b380ea36e66e · outbound

This paper cites V3D: Video Diffusion Models are Effective 3D Generators.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation V3D: Video Diffusion Models are Effective 3D Generators

Reference 8

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source=arxiv_source observed=2026-08-05T22:49:07.059173Z digest=sha256:5f3fb3a41fb4eb72fc369a8a089912156a3fd7c06e7c128a06ba17f813e5fe05

Observation 40fd6c6c-992f-49f5-8295-13f974bfae6b · outbound

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

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation CAT3D: Create Anything in 3D with Multi-View Diffusion Models

Reference 9

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source=arxiv_source observed=2026-08-05T22:49:07.239743Z digest=sha256:9232e68244acd18ec6c6a22392ea60ded92909ddd6155709e1aa8d7c261c75e0

Observation 9134c3c0-b4c9-4dde-9ade-6938ae702ffa · outbound

This paper cites Kl divergence - intuition and examples, n.d.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Kl divergence - intuition and examples, n.d

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-05T22:49:15.295089Z

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=arxiv_source observed=2026-08-05T22:49:07.319504Z digest=sha256:599cbdea5fbf54fa1436119b893adaa4194041320afe1779ec5d724a66aa5b4e

Observation 34cc3b44-4443-44b4-9598-580af0b9e22b · outbound

This paper cites Denoising diffusion probabilistic models.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Denoising diffusion probabilistic models

Reference 11

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source=arxiv_source observed=2026-08-05T22:49:07.430635Z digest=sha256:9ef4158a352ca36b286c4776364e7f54a5469b06cc35c1d747ec10212c4710d3

Observation 362a7ef1-c718-4c32-9e44-bf966aa30687 · outbound

This paper cites Video Diffusion Models.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Video Diffusion Models

Reference 12

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source=arxiv_source observed=2026-08-05T22:49:07.491152Z digest=sha256:95b11c23e20817e162d4564a0045eef387a86b71229949bd0f38b7eff413ed93

Observation 3b9c82fe-cd21-4020-99e5-d718b81a15a8 · outbound

This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 13

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source=arxiv_source observed=2026-08-05T22:49:07.559672Z digest=sha256:479a9d2998d2d79b40e4fa27a4f717dfe3e9b0b625476ceb115dc5302f5d58eb

Observation 9af784f1-7979-45cf-8e6d-0f0b2daa7b43 · outbound

This paper cites Image quality metrics: Psnr vs.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Image quality metrics: Psnr vs

Reference 14

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raw_fallback, observed 2026-08-05T22:49:15.099854Z

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=arxiv_source observed=2026-08-05T22:49:07.751544Z digest=sha256:7b4164c46095c8c418fd99e4bfbfa5e630bde0a67060bb4883e8a222d9ead271

Observation fe485326-5bb9-4fff-acaf-be9e969c5023 · outbound

This paper cites Openclip, 2021.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Openclip, 2021

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-05T22:49:14.941781Z

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=arxiv_source observed=2026-08-05T22:49:07.867005Z digest=sha256:eaaf4f99ff13888f652267990fa85d0869bb4e25bec9e6184d73b95269a2e91c

Observation f91432a2-d029-47d7-a228-04c91db3d28a · outbound

This paper cites Distilling diffusion models into conditional gans.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Distilling diffusion models into conditional gans

Reference 16

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raw_fallback, observed 2026-08-05T22:49:14.801328Z

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=arxiv_source observed=2026-08-05T22:49:07.957042Z digest=sha256:6638ec9e0666a180a1aec5563b948b34c799d9012895ab8f320584e7d33fb5c8

Observation 112879ba-81ec-4eb3-853c-72e4a894b522 · outbound

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

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation 3d gaussian splatting for real-time radiance field rendering

Reference 17

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

source=arxiv_source observed=2026-08-05T22:49:08.094723Z digest=sha256:c53c9d857d755e3159bb191b50f9c71a282fcca1c8386edc9baef01c32c72344

Observation d2464c1b-373e-4108-b78c-54b548f6c43d · outbound

This paper cites Auto-Encoding Variational Bayes.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Auto-Encoding Variational Bayes

Reference 18

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source=arxiv_source observed=2026-08-05T22:49:08.251998Z digest=sha256:17ee26ff41b5453eed013479f338bdbad30a49ed6f917bbf74fa0e1e3fc3a255

Observation 36338b4d-c602-4f19-9dc8-b219e33eaca9 · outbound

This paper cites Tanks and temples: Benchmarking large-scale scene reconstruction.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Tanks and temples: Benchmarking large-scale scene reconstruction

Reference 19

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source=arxiv_source observed=2026-08-05T22:49:08.367876Z digest=sha256:93892faa8d7c976851fd9e872611c49fb8a7d00f7296599a528f5f50aaf0819c

Observation 44528899-6066-4852-b0fc-9eaf75c07512 · outbound

This paper cites EscherNet: A Generative Model for Scalable View Synthesis.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation EscherNet: A Generative Model for Scalable View Synthesis

Reference 20

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source=arxiv_source observed=2026-08-05T22:49:08.446477Z digest=sha256:cd88057f73dd271e421849ddfff3e33a5660c2f58d20d84e03d7685b85bd0576

Observation 28034142-8fc6-4162-9925-8654de2e2477 · outbound

This paper cites Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text

Reference 21

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source=arxiv_source observed=2026-08-05T22:49:08.553160Z digest=sha256:056e6288a836d453dba81dbac55899378dc6e6437ac4a2d79a816b4064eec925

Observation 9020a6c9-732b-42b0-88bb-b3d10f0c4c35 · outbound

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

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Magic3d: High-resolution text-to-3d content creation

Reference 22

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source=arxiv_source observed=2026-08-05T22:49:08.734429Z digest=sha256:f4905f9da4661ccfe9695bf4f65f1c048e6b7fd71586f84741e65fe29a46d515

Observation 4771ef14-03da-4cf0-af5d-444790d99eea · outbound

This paper cites SDXL-Lightning: Progressive Adversarial Diffusion Distillation.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation SDXL-Lightning: Progressive Adversarial Diffusion Distillation

Reference 23

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:08.798273Z digest=sha256:d7d6714b8fb36b4b43ddf4e36d8b85251a76b014f681711cd2f31f4c865ee158

Observation fcefb644-0fa3-4793-99d2-3b97c52ab71a · outbound

This paper cites Diffusion adversarial post-training for one-step video generation.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Diffusion adversarial post-training for one-step video generation

Reference 24

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no resolver link, observed 2026-08-05T22:49:08.859454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:08.859454Z digest=sha256:be79dc5b9e49095245210552b7c5908a3194c5fa7bc623c2c01930ef56ef195b

Observation 2e76425f-7ce7-488b-a8e6-04880587212f · outbound

This paper cites Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision

Reference 25

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verified fuzzy
raw_fallback, observed 2026-08-05T22:49:14.441589Z

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=arxiv_source observed=2026-08-05T22:49:08.951751Z digest=sha256:ccb36eaf5a5f50d8698c0b0979b8726f4935734344e3b41decede5f55d4874a2

Observation b4221563-b261-40e3-9289-ef3846ea0c38 · outbound

This paper cites ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model

Reference 26

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:09.030302Z digest=sha256:d6f9751b8efa18e360622dd78405918966ccc4df5dc7e4a6af1db22635455952

Observation 5dd42d51-3fd1-49e4-981e-61bd92f10e6e · outbound

This paper cites Decoupled Weight Decay Regularization.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Decoupled Weight Decay Regularization

Reference 27

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:09.129158Z digest=sha256:f272568f72657fe7bb17061595d511c602f9b89f78910cc81c3607aa3aa1e869

Observation 9b525dd2-39f4-4673-b845-b3691588f03e · outbound

This paper cites You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs

Reference 28

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verified exact
local_arxiv, observed 2026-08-05T22:49:12.183502Z

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=arxiv_source observed=2026-08-05T22:49:09.201809Z digest=sha256:29b30916071475b547643855636711df65c87138486398851a3eafb5cdac4242

Observation 9621af03-93fb-4040-a57b-8203c0e0b9f9 · outbound

This paper cites On distillation of guided diffusion models.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation On distillation of guided diffusion models

Reference 29

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source=arxiv_source observed=2026-08-05T22:49:09.256432Z digest=sha256:cd9393e03987cd3826af3cfd0f87106de9fe42d7f195d2fc6e043794f504373e

Observation 9050d949-7e8e-4bef-8da3-415a2b4f3745 · outbound

This paper cites Srinivasan, Matthew Tancik, Jonathan T.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Srinivasan, Matthew Tancik, Jonathan T

Reference 30

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:09.323406Z digest=sha256:fae2ee93cc39a86a4e9d5ec6e5d34ace6de7b8875ea9d2235f9e0d7b24a6a25c

Observation 52c1c967-7f6f-4ded-898c-48d0bc2e82c3 · outbound

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

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation DreamFusion: Text-to-3D using 2D Diffusion

Reference 31

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source=arxiv_source observed=2026-08-05T22:49:09.453490Z digest=sha256:727aeb025189f3dff8f5379fa6488e8d915e97f31862546d6e36dfc0d667d37e

Observation 984b2c94-9153-4819-981f-48f363ec7744 · outbound

This paper cites Accelerating 3D Deep Learning with PyTorch3D.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Accelerating 3D Deep Learning with PyTorch3D

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:09.602610Z digest=sha256:079f8551364b5e85b57fad948188e1a59c9a08ba39621bf915342d5a12a79894

Observation d819efd4-686c-4ce2-9ac4-35b466bc53f7 · outbound

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

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation High-resolution image synthesis with latent diffusion models

Reference 33

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:09.695709Z digest=sha256:21ae0526f8e0ade4111e76f3624ff0ea72082a063b8de4ee8b8da638fd017354

Observation b2ade30f-0f88-42bf-9280-6186c0c49e46 · outbound

This paper cites Fast high-resolution image synthesis with latent adversarial diffusion distillation.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Fast high-resolution image synthesis with latent adversarial diffusion distillation

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-05T22:49:14.304849Z

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=arxiv_source observed=2026-08-05T22:49:09.780011Z digest=sha256:27c7a2314c921023d93f95e434c2885ee1d926d84021b4987276635a372fd16e

Observation 3fd67629-299f-4626-85a3-d7f674babdcc · outbound

This paper cites Adversarial diffusion distillation.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Adversarial diffusion distillation

Reference 35

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raw_fallback, observed 2026-08-05T22:49:14.148009Z

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=arxiv_source observed=2026-08-05T22:49:09.853055Z digest=sha256:25c25ce14b3e25e8039386a0dc8caf4f947aa07b3f0c36c2e286e56f0e022cb6

Observation d210bf99-3f08-4b37-ade0-b28739ceff61 · outbound

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

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation MVDream: Multi-view Diffusion for 3D Generation

Reference 36

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no resolver link, observed 2026-08-05T22:49:09.942704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:09.942704Z digest=sha256:e650bdb6de7ca18814928380895aa7263d943d0ad923ac1d299a7a5d7ee536b2

Observation f21b1e7d-34c5-433b-bed7-463c21dec722 · outbound

This paper cites Denoising Diffusion Implicit Models.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Denoising Diffusion Implicit Models

Reference 37

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no resolver link, observed 2026-08-05T22:49:10.034458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:10.034458Z digest=sha256:5382cd2f2652938d9377117ef47c1d99edd44deece7803bd7b2ffffd5f989c83

Observation d4b1df96-e702-4b18-8eca-7de0f637809b · outbound

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

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Score-Based Generative Modeling through Stochastic Differential Equations

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-05T22:49:10.110745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:10.110745Z digest=sha256:779f8512e793ddef3bdc10ea73d63c73d542f141babbc535239b8163b4bc467e

Observation dea1bd94-878c-4924-a780-67966acbaed9 · outbound

This paper cites Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:49:13.993349Z

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=arxiv_source observed=2026-08-05T22:49:10.225567Z digest=sha256:8907ac5eab90ef92794d017ade4c84c18925c4b40f4f316b888030980e24174c

Observation 3e702aaa-6af6-4380-8d94-54a841a632f2 · outbound

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

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:49:13.845494Z

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=arxiv_source observed=2026-08-05T22:49:10.344987Z digest=sha256:7a2e65ef05d3f6030d49029fee2befa71f9d4afde53f8961c8a6f9d3378709fe

Observation e450a08c-d171-457e-b2b4-d2f664789f15 · outbound

This paper cites Dust3r: Geometric 3d vision made easy.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Dust3r: Geometric 3d vision made easy

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:49:13.714394Z

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=arxiv_source observed=2026-08-05T22:49:10.429845Z digest=sha256:4026f3b73ec6d93ce96b0cc5bf0fa2923c88237bc6d198e51fea26ba115e0bb5

Observation 3e53c32a-950b-4d1a-a45b-45813df9ae94 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Image quality assessment: from error visibility to structural similarity

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-05T22:49:10.514992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:10.514992Z digest=sha256:03fd0babf4941f36fb9ec1c6cb5b131453fcefb4b24ffc84b7c8bc782936d6cc

Observation ef22e519-3257-449b-9841-6968c029e8e5 · outbound

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

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-05T22:49:10.594675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:10.594675Z digest=sha256:485990163fa3f583681e472e973711eec28de539d02409a0ef58098832baf19c

Observation f33cb5a5-9b30-4fa4-9eec-2bf03209d88b · outbound

This paper cites Reconfusion: 3d reconstruction with diffusion priors.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Reconfusion: 3d reconstruction with diffusion priors

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:49:13.535001Z

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=arxiv_source observed=2026-08-05T22:49:10.745531Z digest=sha256:c5cc5f0297efbd79d56602701f2c2a6c15422c4129cf1732451952196e59fde5

Observation 038c588e-087d-414e-9eef-f18d10840b66 · outbound

This paper cites Dynamicrafter: Animating open-domain images with video diffusion priors.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Dynamicrafter: Animating open-domain images with video diffusion priors

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:49:13.399267Z

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=arxiv_source observed=2026-08-05T22:49:10.883624Z digest=sha256:be8e8192204d7ceb76b6a9b65aa6b56f611f26fb498108008360f6d1f4d146b1

Observation 801c5dbc-ed47-45b3-a707-815f059ce3f5 · outbound

This paper cites DepthSplat: Connecting Gaussian Splatting and Depth.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation DepthSplat: Connecting Gaussian Splatting and Depth

Reference 46

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unresolved
no resolver link, observed 2026-08-05T22:49:10.960854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:10.960854Z digest=sha256:e6d346df4ac5ddd7b7b6cca9958b0b304637687c24a3b9d5b6924686f8707268

Observation f42e8b8c-8e8a-4ddc-b523-6109ce1cf359 · outbound

This paper cites Ufogen: You forward once large scale text-to-image generation via diffusion gans.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Ufogen: You forward once large scale text-to-image generation via diffusion gans

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:49:13.267398Z

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=arxiv_source observed=2026-08-05T22:49:11.060893Z digest=sha256:a38e847e23d7a7ac595159e6e8b1df042b5c190da4bc312d34589b186ce83478

Observation 8ac66e41-98c1-478c-b674-5d3a22adcb68 · outbound

This paper cites One-step Diffusion Models with $f$-Divergence Distribution Matching.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation One-step Diffusion Models with $f$-Divergence Distribution Matching

Reference 48

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unresolved
no resolver link, observed 2026-08-05T22:49:11.139063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:11.139063Z digest=sha256:985761e3e5603fec40797d1aa65633a46c29b968c86c8c2da42f7535567ad3a2

Observation b048ffd7-175c-46c9-bd69-64f5c3512e67 · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-05T22:49:11.244100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:11.244100Z digest=sha256:4c3c3c0382cde6c326e493d94c707ea789beabf895a0aa5cfef4b7f7f35b802e

Observation 0e2870f5-c1a6-4df3-827b-b26cf1860d0b · outbound

This paper cites One-step diffusion with distribution matching distillation.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation One-step diffusion with distribution matching distillation

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:49:13.085892Z

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=arxiv_source observed=2026-08-05T22:49:11.341947Z digest=sha256:530ba125fe475171c96d6839abc8b87297b3fffef2d168f4969a81613753ae1e

Observation 44416154-6084-4c99-a049-c63cc2fbd271 · outbound

This paper cites From slow bidirectional to fast causal video generators.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation From slow bidirectional to fast causal video generators

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-05T22:49:11.420456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:11.420456Z digest=sha256:f3dd2e60b90eaaa6765e73f5375c06f1b8410d94b2215bc5604eb8d3755a0725

Observation 472279d9-335d-416f-8c4d-03a42993937f · outbound

This paper cites Improved distribution matching distillation for fast image synthesis.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation Improved distribution matching distillation for fast image synthesis

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:49:12.973388Z

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=arxiv_source observed=2026-08-05T22:49:11.512250Z digest=sha256:f5a693252fee37baebd1b096d3f96b36652584e364971688060e53583dca1776

Observation 8ccd2140-1f12-4612-b949-f26d13692c8e · outbound

This paper cites ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-05T22:49:11.559444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:11.559444Z digest=sha256:a7115494841e7d71c0b9425c0950792ce0a8574d07832b3f96001bae3a18cb26

Observation 51f58868-6807-47fe-9d63-ac9292158439 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation The unreasonable effectiveness of deep features as a perceptual metric

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:49:12.797551Z

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=arxiv_source observed=2026-08-05T22:49:11.657782Z digest=sha256:5d64570bd5f176839e0697a7a0259b4d4c6d4cd6debde556b0ccdd88d5b6da27

Pith citing papers

No inbound Pith citation observations are available.