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Paper Citation Record · LEDGER

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

As of 15 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 2026-08-15T06:32:42.880941+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

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=sha256:2634cf19bd6d7e888ff59cc2f083b26ea429020e6e2749cf3e29322e527c4acb

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

source=arxiv_source observed=2026-08-05T22:49:06.452039Z digest=sha256:343d3f716f89261b909e36c84f39fee9986d372fefd028fc0aa8f8f6a02023ec

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

source=arxiv_source observed=2026-08-05T22:49:06.559956Z digest=sha256:b95250ed97b2cdbb2f48e2145d1717bf5e8b149adf5cd6c03d97f6cde98717e0

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

source=arxiv_source observed=2026-08-05T22:49:06.669129Z digest=sha256:e72c33d9b6c676bd75d261638525d65914137175b1ee7934d57342d8a66f34f8

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

source=arxiv_source observed=2026-08-05T22:49:06.796738Z digest=sha256:55b15a0440d1dcc701f210dd9db5f6e74e0c8e74de2c99d93082c97fcc5922d3

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:06.897267Z digest=sha256:dae999eba82407dc24c463ceda69c079c2c5c5d77f151e3c9461a457934586cf

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

source=arxiv_source observed=2026-08-05T22:49:06.977488Z digest=sha256:663cd3bcd9ddbb62ee2f8bb236ff2d2eacc86fc578634b93fc0a86d78206e434

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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no resolver link, observed 2026-08-05T22:49:07.059173Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-05T22:49:07.059173Z digest=sha256:4c4390dd210c3a3e7c746070aaf2cb782ca67c851894a08c7bc4d384a3c19d80

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:a2d89c76724c04de6df4fc04d54660834310789473e4cce2ea0511f041f88345

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

source=arxiv_source observed=2026-08-05T22:49:07.319504Z digest=sha256:3db66673ff926ee48bb1d79ba5ddfc3d319fb966dca77f2267fcbf970f9372e1

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=sha256:8b8039846672dc2841309f12d0e59df91378a0f53a8880c91943f2c612177ed5

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=sha256:4ec79b85a3d7e89955dd80a1d7a377fb045d0e1ef3fa6d128464e503884cd2f0

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=sha256:011a46cfe24291991bd93ba563636fe896e4aa54711d1f02bbeee414603a967d

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

source=arxiv_source observed=2026-08-05T22:49:07.751544Z digest=sha256:042747e64b9575723b63d03c6e288ca2b13998ab4e5ba1c779c1016720f4629e

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

source=arxiv_source observed=2026-08-05T22:49:07.867005Z digest=sha256:f1f0f74317af6949a7b6e4890c61059251c1a515c81ca782bcc74ba01e12836d

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

source=arxiv_source observed=2026-08-05T22:49:07.957042Z digest=sha256:0e4c44b22081c604a1a5cbc077641b3eb4b535da54438068412d962f782c3af5

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

source=arxiv_source observed=2026-08-05T22:49:08.094723Z digest=sha256:45d15ea7e6967cd251a10870474681f8d8a57da99eff98abd69130893552bb1f

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:c819caf8a0ad3b039c307aa33bd840de753e1b739958f69c1848492bf40fdb71

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=sha256:328654267d2fdb88e202df5fbfe8e067fa48f386ac347a6236412d4c4f0aae2b

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=sha256:9ae997dcf9da1184737e72dca1bb72eed7112cf2a40b43c5b7bb0b24d5c30023

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:e746a08efc60f2d69f72cfba33436eeece40dd81f126fe230af130162936ddfa

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:08.734429Z digest=sha256:bb15ad7a3b5c72cefee3a86ec6adc877c56ddc097e051548bd7c80fa2e6ba99c

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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source=arxiv_source observed=2026-08-05T22:49:08.798273Z digest=sha256:1727004056fc1e360edf23c99a597a70d03bcdf50fad997095cdbce2dcad4e12

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:f0bfc4716ae4755e7a3c4afa5a754ceda3f12e270033444f88b547c7c60cf78d

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

source=arxiv_source observed=2026-08-05T22:49:08.951751Z digest=sha256:ccfa98ce665cf94b5b7561a86403fe4c36cfe2a3216cc1d5df46b4f41f803d1c

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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source=arxiv_source observed=2026-08-05T22:49:09.030302Z digest=sha256:d7a95cd2fd88facd703917ca30a2156f77252ec91cd383bf0c62d0ce18007ee4

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=sha256:7e793d9cfeb05ca140c7caa8dfa3482100f7ae811bfc162656a8f811be95a816

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

source=arxiv_source observed=2026-08-05T22:49:09.201809Z digest=sha256:d630338a4b1f3526c8baa61feea5a0d80b6620f20eef8e0f2b544d3644355259

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:09.256432Z digest=sha256:9e78ae54017e31d879bc08e213f36287a916909fe6be659cc934a470160b3ff2

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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source=arxiv_source observed=2026-08-05T22:49:09.323406Z digest=sha256:a94b9ca97469ea5dd32d88d96be20a7da328e6749ffffeebc0c97d263213089d

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:d7e7d337ba1acd5867844a2156b344a46ba0d4b63be6883eea11c0fe3f86ee40

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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no resolver link, observed 2026-08-05T22:49:09.602610Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:09.602610Z digest=sha256:b1e3087f8d9fd566382b24b49cd8a21fe8551a5c5c6a25701ad2db8bdb956150

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:d1e2b2d48de61dbe6fb68e45fb500134bbdeff4eaab906bd94034af000a9177f

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

source=arxiv_source observed=2026-08-05T22:49:09.780011Z digest=sha256:fa80460d5af664767b2d99776e7f36d241d243bc6153efbe0f06ab039f9dae32

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

source=arxiv_source observed=2026-08-05T22:49:09.853055Z digest=sha256:353cb64f55a9ce7e8ccbbfd243c7586c67ffd92f71103d5aff6cac76367f2890

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:49:09.942704Z digest=sha256:72ac1ec2ce9202fe876269587081a1bf5b102ef64baadaa4fc214bc010670c67

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=sha256:43aa5d507e960b8b32185f70a14507cb4fd2b2435878992c3599f008863df9bb

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

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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:c81b952593db1489df8bf3ed58f6bac09721012118c2bed90b65ca9cb3d4cc9d

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

source=arxiv_source observed=2026-08-05T22:49:10.225567Z digest=sha256:1d24c148b408a5128e82240641cbe5bc1efe0844bd0dbbd936a6ad4b9bbc62d8

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

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

source=arxiv_source observed=2026-08-05T22:49:10.344987Z digest=sha256:d5bc026772bd340ce09d42869dba720d2aec18a46286f5af5e0ce7a117b40376

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

source=arxiv_source observed=2026-08-05T22:49:10.429845Z digest=sha256:765be3f3e84a4d6754426b77c3b2d285871d914b7766a06837c980487a2f0d96

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

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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:aba701c2c012b7c9960e3b05a146b8c563bf9f248445b098ce3e657ffb4e4310

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=sha256:2088a68fc8adcb37088f78e35eb6bdeb3149801056c0b791ea96207b6ba74259

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

source=arxiv_source observed=2026-08-05T22:49:10.745531Z digest=sha256:d259847011a609f2b70cbec68ce6d4ecf687e02f36fe9b8fd3c3759e6ea9d10d

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

source=arxiv_source observed=2026-08-05T22:49:10.883624Z digest=sha256:f436a01414b99926d604d57283f0bc3b70088072c8afde6adf681c91016b7560

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

Resolution
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:cede14fb8215ae7654dd3dfad470b397ed29255855577dfac7ab25a257ec083c

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

source=arxiv_source observed=2026-08-05T22:49:11.060893Z digest=sha256:9d378e9c78bbd797351a955f23046cd31b000e68423e215f6702ba91c4021ed7

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

Resolution
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:c7d81a4c40390c4512cada319ad234328831584b8929a34380d0d3633063615a

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:ba1e68ce516b2d3ef45b5c2417521360b1f6ace3a706fc9d34760e2443109537

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

source=arxiv_source observed=2026-08-05T22:49:11.341947Z digest=sha256:5ab4799797a14a9c4e07dff82225d160e94ca8d7967056f74724fe6385e58e73

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=sha256:3694d553629fd1db5281b5dacf33a521bdc6ed66ff63ee177f32058b1b037f07

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

source=arxiv_source observed=2026-08-05T22:49:11.512250Z digest=sha256:63b6aa2eaaf7db245a95bb4ba90647fbee681104508cc80eb947b10993110b2a

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=sha256:946c8c0ebec4630384117b75dbf3b0e05490154041259d0ba90be18e9741c1d3

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

source=arxiv_source observed=2026-08-05T22:49:11.657782Z digest=sha256:58c5e8a373c938178180b22b082bf63f5e79286bf925a2a7299c2d6560029d98

Pith citing papers

No inbound Pith citation observations are available.