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Paper Citation Record · LEDGER

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning

As of 23 August 2026, this Paper Citation Record lists 100 of 137 outbound references and 3 inbound Pith citation observations for arXiv:2412.02114.

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

pith.paper-citation-record.v1
2412.02114 v2

Coverage vector

measured 100 of 137 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:53:47.353461Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:19:03.421561Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T20:17:55.853675Z

Reference resolution

100 of 137 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved97
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 33eadf74-b878-433c-b5f3-46596b368fd9 · outbound

This paper cites UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing

Reference 1

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source=pdf_text observed=2026-08-11T23:53:46.781364Z digest=sha256:19e3786af9af970917466b5aa3b0e783388eb4f4b98397384cbcaf6899700b9f

Observation a8301146-224f-41cf-a4fa-9ff8ce7bab40 · outbound

This paper cites Text2live: Text-driven layered im- age and video editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Text2live: Text-driven layered im- age and video editing

Reference 2

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source=pdf_text observed=2026-08-11T23:53:46.787614Z digest=sha256:7d60172aba8e16d5688752aa55d0e737e20223ab4b1558de78825ef64a7b30ff

Observation ffa7df74-28c2-4f41-993f-062ebfcea146 · outbound

This paper cites Is space-time attention all you need for video understanding? In ICML, page 4, 2021.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Is space-time attention all you need for video understanding? In ICML, page 4, 2021

Reference 3

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source=pdf_text observed=2026-08-11T23:53:46.793167Z digest=sha256:8b27b34ff17783e434d5feea3f79afab5ad85d87e9372a754f316304d0a85336

Observation 2c690528-9c4d-40c9-bc25-f8b9cb553493 · outbound

This paper cites Ledits++: Limitless image editing us- ing text-to-image models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Ledits++: Limitless image editing us- ing text-to-image models

Reference 4

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source=pdf_text observed=2026-08-11T23:53:46.799382Z digest=sha256:a39aa1f24e38e113e0918196ab17ec362b2156c7c00bb213be3c65383f83a562

Observation 2729ecce-e6e8-4639-a528-ce5fc29acfbe · outbound

This paper cites In- structpix2pix: Learning to follow image editing instruc- tions.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning In- structpix2pix: Learning to follow image editing instruc- tions

Reference 5

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source=pdf_text observed=2026-08-11T23:53:46.804878Z digest=sha256:83b553568ffa76dda6add8f0de43348f543a4eeab990b2bfe2556ae678e3b457

Observation f23dacf9-8af9-4430-9dcb-18393ee657b8 · outbound

This paper cites Video generation models as world simulators.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Video generation models as world simulators

Reference 6

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source=pdf_text observed=2026-08-11T23:53:46.810879Z digest=sha256:c68184355ee7548848907e81c66110c0af774df3c78f673c7565e0cafd2677dd

Observation 8ad14e2b-4500-4b4e-beca-3c52ae198b7e · outbound

This paper cites Pix2video: Video editing using image diffusion.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Pix2video: Video editing using image diffusion

Reference 7

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source=pdf_text observed=2026-08-11T23:53:46.816569Z digest=sha256:a1826c33bd313e476c223e13cac045a5b9c288064e2bbb8433b12c7bb976e79f

Observation 891e0974-8b6f-4b7f-873a-65d9e89e49ff · outbound

This paper cites Sta- blevideo: Text-driven consistency-aware diffusion video editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Sta- blevideo: Text-driven consistency-aware diffusion video editing

Reference 8

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source=pdf_text observed=2026-08-11T23:53:46.822825Z digest=sha256:f19c5deeb2b37e7db3e3688c25ff0af611925a41cd02a9dc30709b22a6b1977b

Observation 7dcf7bf1-8e84-42f1-8b01-dc366143ce2f · outbound

This paper cites DiffusionAtlas: High-Fidelity Consistent Diffusion Video Editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning DiffusionAtlas: High-Fidelity Consistent Diffusion Video Editing

Reference 9

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source=pdf_text observed=2026-08-11T23:53:46.828078Z digest=sha256:9ab6e5033319ddf7d7de6a60d031f26bfe62243b25d87f7b0c7055d6be0ce840

Observation bc80270f-5c72-428a-b28c-b366f73a1a22 · outbound

This paper cites VideoCrafter1: Open Diffusion Models for High-Quality Video Generation.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

Reference 10

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source=pdf_text observed=2026-08-11T23:53:46.833420Z digest=sha256:634043497c9bb389dac40ffa147cdac37f6464be17a79b8a1e5191694ceeabfb

Observation 3dda0583-6de2-43ab-b500-f4bf891132fc · outbound

This paper cites Finecliper: Multi-modal fine- grained clip for dynamic facial expression recognition with adapters.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Finecliper: Multi-modal fine- grained clip for dynamic facial expression recognition with adapters

Reference 11

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source=pdf_text observed=2026-08-11T23:53:46.839517Z digest=sha256:4bc2e1d4562326b3464e9e94be5028167f6edda14ffb706a2e1d088c0f30460e

Observation 80ffce92-1018-40ad-994c-6b168c88c4f1 · outbound

This paper cites GaussianVTON: 3D Human Virtual Try-ON via Multi-Stage Gaussian Splatting Editing with Image Prompting.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning GaussianVTON: 3D Human Virtual Try-ON via Multi-Stage Gaussian Splatting Editing with Image Prompting

Reference 12

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source=pdf_text observed=2026-08-11T23:53:46.845250Z digest=sha256:893654bc53283f6d9aba96938dfaffcb8c292ca9be2de36d02bd576eecafdd33

Observation a6b3de45-8c05-4138-b743-8889fe222c26 · outbound

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

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Videocrafter2: Overcoming data limitations for high-quality video diffu- sion models

Reference 13

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source=pdf_text observed=2026-08-11T23:53:46.851445Z digest=sha256:7eaf5d89f7b71568ac39334ded4310afbbc109f591b11c5259d3e2dc0a29e388

Observation e2641118-cd05-458b-89ab-45c16e61e611 · outbound

This paper cites NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T23:53:46.856463Z digest=sha256:8526ae76a2e7b448ee11a88c928ed3762f992863d5c08a2462e0114577c5b035

Observation 1e0176e4-b2a3-44ef-a040-7c987fef9447 · outbound

This paper cites Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning

Reference 15

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source=pdf_text observed=2026-08-11T23:53:46.862656Z digest=sha256:89077649dccddc5a22102e59a5c1af64b9fdc84949dfffa67688d62fa8b9bfda

Observation 624e8f0d-0a8f-48e4-bcb5-67e6aad1615b · outbound

This paper cites UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics

Reference 16

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source=pdf_text observed=2026-08-11T23:53:46.868411Z digest=sha256:a1a614b094217517cd1c6dace0671d22db0f354c1baca03e4fe0f1d559b274d6

Observation db8da452-b77d-4430-9551-d9b66b49099a · outbound

This paper cites Consistent Video-to-Video Transfer Using Synthetic Dataset.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Consistent Video-to-Video Transfer Using Synthetic Dataset

Reference 17

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source=pdf_text observed=2026-08-11T23:53:46.874322Z digest=sha256:46361085e96756aa2a77de9fa16d95a272783839469fb5297175120194cab3e0

Observation a1a64e38-1a7c-454e-bbeb-f9aed67355bc · outbound

This paper cites Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models

Reference 18

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source=pdf_text observed=2026-08-11T23:53:46.879585Z digest=sha256:ddaa071ad42ca26361c9d7ec2d7d6aa06fc2ed1e0a914e4b9eed9e60617da8ec

Observation 78f70428-b488-4779-a15f-2680f5db1b06 · outbound

This paper cites FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing

Reference 19

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source=pdf_text observed=2026-08-11T23:53:46.884734Z digest=sha256:da4d136e30a01d581e1499e5336d60edc92667c4baadd5d70800132aa43fe28e

Observation 8c81e810-3aad-49ec-9043-630fe60244d4 · outbound

This paper cites DiffEdit: Diffusion-based semantic image editing with mask guidance.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning DiffEdit: Diffusion-based semantic image editing with mask guidance

Reference 20

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source=pdf_text observed=2026-08-11T23:53:46.890640Z digest=sha256:3f2806a5c7c249ce9352701b56d5bc22f3f95b932115d306b60cac3d18cb9765

Observation fd88eb98-0884-4304-af15-fbd6806705ce · outbound

This paper cites Videdit: Zero-shot and spatially aware text-driven video editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Videdit: Zero-shot and spatially aware text-driven video editing

Reference 21

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source=pdf_text observed=2026-08-11T23:53:46.899402Z digest=sha256:21d0b1884c0ae1bf32097c6cfb26c66812ba3e7bdfe5093675c590437db75792

Observation a59ea200-8f77-4e0b-a15c-79899eb59903 · outbound

This paper cites Autoregressive Video Generation without Vector Quantization.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Autoregressive Video Generation without Vector Quantization

Reference 22

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source=pdf_text observed=2026-08-11T23:53:46.905127Z digest=sha256:afb9fe2b94b7de80151715bc6357251c04a70f0abf686af17168fef559947581

Observation 11318c00-6a0e-4d6e-b434-2900afc473ae · outbound

This paper cites Structure and content-guided video synthesis with diffusion models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Structure and content-guided video synthesis with diffusion models

Reference 23

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source=pdf_text observed=2026-08-11T23:53:46.912020Z digest=sha256:7007a2cde40bdb50704d1819954111f60159aa49dfe71935ebd469f3982a83cf

Observation ab69298d-8ace-488b-912f-605150aa6c98 · outbound

This paper cites Videoshop: Localized Semantic Video Editing with Noise-Extrapolated Diffusion Inversion.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Videoshop: Localized Semantic Video Editing with Noise-Extrapolated Diffusion Inversion

Reference 24

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source=pdf_text observed=2026-08-11T23:53:46.917361Z digest=sha256:e7141cda74e4e5d813514ff9a9e45dcd4c87bdbe069fdeb0a0a9bdcacc2a9e95

Observation a46ab9fd-874d-49f4-b943-51141a6d22c4 · outbound

This paper cites ViViD: Video Virtual Try-on using Diffusion Models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning ViViD: Video Virtual Try-on using Diffusion Models

Reference 25

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source=pdf_text observed=2026-08-11T23:53:46.923268Z digest=sha256:09f509e76fbe41ceacbbd44c6c1b74464797cf0cb2c0b3090434513c87701caa

Observation 68ae1d73-2618-474d-9d81-33618874c93b · outbound

This paper cites X3d: Expanding architectures for efficient video recognition.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning X3d: Expanding architectures for efficient video recognition

Reference 26

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source=pdf_text observed=2026-08-11T23:53:46.928669Z digest=sha256:cef80cf87a8338438202fb8f2cef9c49fe4d36740a17a1d11bed6c645ee3c78a

Observation 9d76ab8c-eee9-4a52-a41a-444fb862ddfc · outbound

This paper cites Ccedit: Creative and controllable video editing via diffu- sion models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Ccedit: Creative and controllable video editing via diffu- sion models

Reference 27

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source=pdf_text observed=2026-08-11T23:53:46.935250Z digest=sha256:8d6ec4dfb3e6a218c6a1e6b4cea8648c480b851255987bb343c40714422f647e

Observation 99866062-3cf9-4a28-87d4-02d787d1c45b · outbound

This paper cites Wave: Warping ddim inversion features for zero-shot text-to-video editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Wave: Warping ddim inversion features for zero-shot text-to-video editing

Reference 28

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source=pdf_text observed=2026-08-11T23:53:46.940144Z digest=sha256:8627e68aceccef23365b3fd79764158d5ff4976771f1428bf3070d054ec4c94a

Observation 097f4250-312c-43e8-aaee-4b38135f2f6a · outbound

This paper cites Instructdiffusion: A gener- alist modeling interface for vision tasks.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Instructdiffusion: A gener- alist modeling interface for vision tasks

Reference 29

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source=pdf_text observed=2026-08-11T23:53:46.945200Z digest=sha256:3de7fb7a9c15524d19b4381525ae44057de41ba5aed99731fd4aa7d924f84a08

Observation 39af13f7-41d3-465b-b7a4-be8bb1ca62ed · outbound

This paper cites TokenFlow: Consistent Diffusion Features for Consistent Video Editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning TokenFlow: Consistent Diffusion Features for Consistent Video Editing

Reference 30

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source=pdf_text observed=2026-08-11T23:53:46.950652Z digest=sha256:17da2e6eb5b393648265f28d6bbc71c9043a13c4533937afb1a92b9e32e25f36

Observation 2a4157ca-248a-4815-aefb-61535fb42718 · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 31

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source=pdf_text observed=2026-08-11T23:53:46.956427Z digest=sha256:9155ee23618d8d1c58fc71dfb38778e36e0291318d5fb8f6921f93f0eda2319e

Observation b62a54b1-c7b4-4866-8406-ee3aa9d8d8f5 · outbound

This paper cites Proxedit: Improving tuning-free real image editing with proximal guidance.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Proxedit: Improving tuning-free real image editing with proximal guidance

Reference 32

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source=pdf_text observed=2026-08-11T23:53:46.962316Z digest=sha256:db53778cd0a72cb3a793a1a456d9a327217bde213c33e30190780b4e90cfbcc0

Observation ff0e5600-1954-4947-b264-f1178ba81245 · outbound

This paper cites Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control

Reference 33

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source=pdf_text observed=2026-08-11T23:53:46.967995Z digest=sha256:f3d60b15ae7c32a317d72c2e43f9710f938b109c5b13b5e283302a7438d5d340

Observation 143e62b9-3606-4b0b-9c91-c26431f81091 · outbound

This paper cites CameraCtrl: Enabling Camera Control for Text-to-Video Generation.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning CameraCtrl: Enabling Camera Control for Text-to-Video Generation

Reference 34

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source=pdf_text observed=2026-08-11T23:53:46.974605Z digest=sha256:2693f1652b55a604347235266a486027f586f0b27ec561f16970be3e46a2a26d

Observation d4eef621-065a-45ce-bde9-3a893c7a78b0 · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 35

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source=pdf_text observed=2026-08-11T23:53:46.979728Z digest=sha256:b94343d8fc56115620094363df604662b240bbdb55018b62d34820066e1a736e

Observation b1a17db6-35e1-4a09-b58f-d67c044958cf · outbound

This paper cites Classifier-Free Diffusion Guidance.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Classifier-Free Diffusion Guidance

Reference 36

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source=pdf_text observed=2026-08-11T23:53:46.985572Z digest=sha256:b6d0eca6f1889e6b1278fa421f3aa194b09173ab2ce9ce2b30218c7efaecb80b

Observation 9a55dafa-b016-481d-939f-0ca2a7ac0b29 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Denoising dif- fusion probabilistic models

Reference 37

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source=pdf_text observed=2026-08-11T23:53:46.991468Z digest=sha256:f982a4bd8fe3422b7d2f87cfea43a04a2fb6a81b8a267c7a48461f2caf9b8d5f

Observation 8787795d-cb4e-4a26-adb7-591ff304044b · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Imagen Video: High Definition Video Generation with Diffusion Models

Reference 38

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source=pdf_text observed=2026-08-11T23:53:46.996818Z digest=sha256:6fee25e43a14dfd1f7bf2c1c1830f477ab52c4c020caa024a5904ca4c599c25f

Observation d2ce0593-90e4-4238-9dd5-9079d12b7604 · outbound

This paper cites Video dif- fusion models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Video dif- fusion models

Reference 39

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source=pdf_text observed=2026-08-11T23:53:47.002895Z digest=sha256:564243b768dac6695b596bb2b53f2f9318674c3b7b00ec3d8bd165c8760df7b1

Observation 95da9dc4-d68a-49e3-be7e-c324dc485cfd · outbound

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

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 40

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source=pdf_text observed=2026-08-11T23:53:47.009066Z digest=sha256:e12d4cd31afb84c40ce2c9b148107191d71ffb2796176be8c848a4fa62510a3d

Observation d47ffb53-2b00-4c70-b17e-de4726d1cd54 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning LoRA: Low-Rank Adaptation of Large Language Models

Reference 41

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source=pdf_text observed=2026-08-11T23:53:47.015853Z digest=sha256:8b03934d9882c6580055fa1ef921ea2852bf970e5b2ecf1217cabb0397f3ce38

Observation f3f4e150-03ac-43b4-9ac6-ebc93de20ee6 · outbound

This paper cites VideoControlNet: A Motion-Guided Video-to-Video Translation Framework by Using Diffusion Model with ControlNet.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning VideoControlNet: A Motion-Guided Video-to-Video Translation Framework by Using Diffusion Model with ControlNet

Reference 42

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source=pdf_text observed=2026-08-11T23:53:47.022250Z digest=sha256:78fd1c169dae945abfd1cd75c955b7854dcf83fb41ef306b1455692275762fd5

Observation 436269ae-c941-4307-8144-894f0e2fc2b4 · outbound

This paper cites Crest: Cross-modal resonance through evidential deep learning for enhanced zero-shot learning.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Crest: Cross-modal resonance through evidential deep learning for enhanced zero-shot learning

Reference 43

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source=pdf_text observed=2026-08-11T23:53:47.029444Z digest=sha256:2fb93a96a5ec77e17044f46573a32959ff89d603e636548073ceb31f9dd2977e

Observation b56b5ddf-26ca-4544-8840-6c13ba480074 · outbound

This paper cites Diffusion Model-Based Image Editing: A Survey.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Diffusion Model-Based Image Editing: A Survey

Reference 44

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source=pdf_text observed=2026-08-11T23:53:47.034932Z digest=sha256:3d1deb9b05eb8da536c60663e521c18554adbc708741a5680ad045318522aa27

Observation a07b8b4d-4263-4380-a0d9-268a795b1998 · outbound

This paper cites VBench: Comprehensive benchmark suite for video generative mod- els.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning VBench: Comprehensive benchmark suite for video generative mod- els

Reference 45

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source=pdf_text observed=2026-08-11T23:53:47.041189Z digest=sha256:b102200329c1039f831681a1c2669399b3618ed58f3ec97a278204abb2a9f4b8

Observation 845217a1-61ad-4608-b9e4-b0419eb68efb · outbound

This paper cites Perceiver io: A general architecture for structured inputs & outputs.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Perceiver io: A general architecture for structured inputs & outputs

Reference 46

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source=pdf_text observed=2026-08-11T23:53:47.046920Z digest=sha256:1c9d5f09797564cb30f8345f17086cb3220baddae05a6cd8c33c5b25083c3a57

Observation 2c803d26-437a-4c0a-b41a-78b7c9e102d3 · outbound

This paper cites Perceiver: General perception with iterative attention.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Perceiver: General perception with iterative attention

Reference 47

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source=pdf_text observed=2026-08-11T23:53:47.053725Z digest=sha256:619d309a0c00400634ee3a6de3bb0d01a5f3347fc1151a2ddf15b9e5865edb3a

Observation d4e5321e-7094-4ed6-9609-52aa47e10fe9 · outbound

This paper cites Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models

Reference 48

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source=pdf_text observed=2026-08-11T23:53:47.059150Z digest=sha256:19fa5957ca2ca8b29a1907154f18170b5d250fcaddb5cec9443f057d3b32dcb7

Observation 0e3bbe4f-df04-49b2-b815-5e41c6b263bc · outbound

This paper cites DreamMotion: Space-Time Self-Similar Score Distillation for Zero-Shot Video Editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning DreamMotion: Space-Time Self-Similar Score Distillation for Zero-Shot Video Editing

Reference 49

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source=pdf_text observed=2026-08-11T23:53:47.065543Z digest=sha256:ec90fbce5bc2ddefa94c8a3478a672587d60abee891ad7b91ded8578634df0b4

Observation d27f502b-806d-414c-9167-648360eb58d1 · outbound

This paper cites Vmc: Video motion customization using temporal attention adaption for text-to-video diffusion models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Vmc: Video motion customization using temporal attention adaption for text-to-video diffusion models

Reference 50

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source=pdf_text observed=2026-08-11T23:53:47.071249Z digest=sha256:6431456aa27bc219c297147ddfec7ccd40dc14ad4a4f58f3dcf1c5931ee6bdcf

Observation d823c53b-bc00-48b7-ab8f-da0b4fde9f64 · outbound

This paper cites Object-Centric Diffusion for Efficient Video Editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Object-Centric Diffusion for Efficient Video Editing

Reference 51

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source=pdf_text observed=2026-08-11T23:53:47.076881Z digest=sha256:02dff282861114d813ef677b43f39551e50e73cd5192bb9ee66ff4a0c86e359d

Observation f579eea4-8bc9-41f4-891c-558f15decfca · outbound

This paper cites Rave: Randomized noise shuf- fling for fast and consistent video editing with diffusion models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Rave: Randomized noise shuf- fling for fast and consistent video editing with diffusion models

Reference 52

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source=pdf_text observed=2026-08-11T23:53:47.084147Z digest=sha256:1600ed7874083b4142cd76a01d651be63f0297d478fedc698831d2d5ecf489e4

Observation 262c74b4-da25-49e0-bae8-4dea70d09e8f · outbound

This paper cites Imagic: Text-based real image editing with diffusion mod- els.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Imagic: Text-based real image editing with diffusion mod- els

Reference 53

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source=pdf_text observed=2026-08-11T23:53:47.089499Z digest=sha256:ea939316d3ab0e31010b066d3a7ee54eb53baaad7eff321780380bec6208a2b8

Observation ce04f833-535b-4e61-837a-4a1e2a81f3e2 · outbound

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

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Text2video-zero: Text- to-image diffusion models are zero-shot video generators

Reference 54

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source=pdf_text observed=2026-08-11T23:53:47.094825Z digest=sha256:f039b609bdef1176ea889ebe76ba8ecbdfc254d927193301fa1b3072ee62498c

Observation 68ead89d-9617-4888-a2d8-d4c020198ce9 · outbound

This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 55

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source=pdf_text observed=2026-08-11T23:53:47.100317Z digest=sha256:8d8fa1e7a93221fd90324139f4655a0d8385cae023df247e5e50cbfca94097b4

Observation 7adc89c2-2f21-4d84-9346-1a221929985b · outbound

This paper cites HunyuanVideo: A Systematic Framework For Large Video Generative Models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning HunyuanVideo: A Systematic Framework For Large Video Generative Models

Reference 56

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source=pdf_text observed=2026-08-11T23:53:47.105488Z digest=sha256:b81778de45d3d7d0f31f4f7f520e6bbc6ba86155bab8729aaf6468455f5be9e9

Observation dc9b874e-8288-40d7-b8b2-c64768931de1 · outbound

This paper cites AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks

Reference 57

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source=pdf_text observed=2026-08-11T23:53:47.111393Z digest=sha256:508da24472b2301cdf9e4e05fcd0c7099f108d7245a43a832d760cf1b807409d

Observation 3616f32a-4199-4e45-89e3-9094b2e6b624 · outbound

This paper cites Shape-aware text-driven lay- ered video editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Shape-aware text-driven lay- ered video editing

Reference 58

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source=pdf_text observed=2026-08-11T23:53:47.116861Z digest=sha256:232657b64dfbc1a508f04fac074285b054f5eaec9d206b47860d736ac72f3e65

Observation b12d74fd-258a-4783-9942-d3357d47ebbe · outbound

This paper cites MiniMax-01: Scaling Foundation Models with Lightning Attention.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning MiniMax-01: Scaling Foundation Models with Lightning Attention

Reference 59

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source=pdf_text observed=2026-08-11T23:53:47.121750Z digest=sha256:5259e907072ea3152f34e1c708763166776c8654540be1d2dfbf3cbaee0a2c5b

Observation c3f69115-1763-4c93-9686-fd793b7fa417 · outbound

This paper cites Vidtome: Video token merging for zero-shot video editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Vidtome: Video token merging for zero-shot video editing

Reference 60

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source=pdf_text observed=2026-08-11T23:53:47.127131Z digest=sha256:17e584ad44cd445197d780abacbddf30cdc9e6a6b0334e14ed4805e53cc18849

Observation ea155bd8-d6b8-42bb-bee4-2ae91502ece5 · outbound

This paper cites Flowvid: Taming imperfect op- tical flows for consistent video-to-video synthesis.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Flowvid: Taming imperfect op- tical flows for consistent video-to-video synthesis

Reference 61

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source=pdf_text observed=2026-08-11T23:53:47.132041Z digest=sha256:bc37b2249077955b18b6dd060c9ef163f8d006af1d8c17c74b11aba9564b2e85

Observation b4f49ed0-a9b2-49b6-a752-9d9befda40fe · outbound

This paper cites MagicEdit: High-Fidelity and Temporally Coherent Video Editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning MagicEdit: High-Fidelity and Temporally Coherent Video Editing

Reference 62

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source=pdf_text observed=2026-08-11T23:53:47.137361Z digest=sha256:dba93c452192500580874c356ef1ab2fc157fb6de7f4d6fe6746f254d4c4d4dd

Observation c5a2dec2-9b53-4b72-bf45-bcfbb5c42231 · outbound

This paper cites Open-Sora Plan: Open-Source Large Video Generation Model.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Open-Sora Plan: Open-Source Large Video Generation Model

Reference 63

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source=pdf_text observed=2026-08-11T23:53:47.143349Z digest=sha256:ac4500af0c7a4ca3c9e1fa1233f6170354894debb61dfc6423c4637008f9c697

Observation 48e3c1b0-6ad2-4c1e-9846-5713aad5f266 · outbound

This paper cites MotionClone: Training-Free Motion Cloning for Controllable Video Generation.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning MotionClone: Training-Free Motion Cloning for Controllable Video Generation

Reference 64

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source=pdf_text observed=2026-08-11T23:53:47.148664Z digest=sha256:1564d238fe08313ee350b77efa9cd644045eddad5f7f3d630b39407495c7d834

Observation e4b1926e-7245-4982-8b31-654411f3501c · outbound

This paper cites Video-p2p: Video editing with cross-attention con- trol.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Video-p2p: Video editing with cross-attention con- trol

Reference 65

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source=pdf_text observed=2026-08-11T23:53:47.154111Z digest=sha256:d1e1e113e3e5b518d9b75d35ce23d28ed0e15d62d2ecd6dfccd6d62e50d062d4

Observation 8a239b10-2566-475b-b4c5-321821a31136 · outbound

This paper cites Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Grounding.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Grounding

Reference 66

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source=pdf_text observed=2026-08-11T23:53:47.160014Z digest=sha256:2a3e0713ed653583fa820cd05508295ed88470fa75bd1a71c0f59fad4fff15b5

Observation 026852fa-269c-4a09-ac71-133110eb41f5 · outbound

This paper cites Follow your pose: Pose- guided text-to-video generation using pose-free videos.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Follow your pose: Pose- guided text-to-video generation using pose-free videos

Reference 67

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source=pdf_text observed=2026-08-11T23:53:47.165899Z digest=sha256:22e00a5a5bbe3e361b992c5620ce8aa254b159e4144d1a83924aab21a233b6c8

Observation 435a1792-08a6-4e66-a5fd-b9942d01dda9 · outbound

This paper cites SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 68

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source=pdf_text observed=2026-08-11T23:53:47.170916Z digest=sha256:b005e3b777536fcd9fe67a0d7936a30d9749879553ccfca540b06d85e4a02e77

Observation a9ad3005-262c-47c2-9072-138a88b8628a · outbound

This paper cites Null-text inversion for editing real im- ages using guided diffusion models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Null-text inversion for editing real im- ages using guided diffusion models

Reference 69

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source=pdf_text observed=2026-08-11T23:53:47.176242Z digest=sha256:317356439bc4e600395d8a1edd2e56a7b3ae5b0c459493c141a8ec8a59977f85

Observation 3cc427c7-874a-4167-a037-d82c44fec939 · outbound

This paper cites Dreamix: Video Diffusion Models are General Video Editors.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Dreamix: Video Diffusion Models are General Video Editors

Reference 70

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source=pdf_text observed=2026-08-11T23:53:47.181974Z digest=sha256:85a85de5457035c2de40362c2909bf25155984be12d2a0580850a4c6eb4a80eb

Observation 91eb9d30-1c0c-4caa-9e87-401ebf89cfa9 · outbound

This paper cites ReVideo: Remake a Video with Motion and Content Control.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning ReVideo: Remake a Video with Motion and Content Control

Reference 71

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source=pdf_text observed=2026-08-11T23:53:47.188181Z digest=sha256:ba70a283bdc34cb30ae9ced0709ccb031571d9eb1c933527c72ad58b95f7e48e

Observation 41d73797-4aa0-46ad-a292-6203afb8cf67 · outbound

This paper cites OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation

Reference 72

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source=pdf_text observed=2026-08-11T23:53:47.194030Z digest=sha256:704c63bd99dde704706493f19cd67e87e334350aa1fb6911858aaeca98172b99

Observation 746874c8-7511-4da8-bd31-fe6a069c75ca · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 73

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source=pdf_text observed=2026-08-11T23:53:47.200286Z digest=sha256:22d1b40cd41abd116dbc3e8140d9dfab353b8692c19993ef18e215908658706b

Observation ab521d10-444b-41e9-966d-e51bf36878f3 · outbound

This paper cites Gpt-4 system card.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Gpt-4 system card

Reference 74

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source=pdf_text observed=2026-08-11T23:53:47.205562Z digest=sha256:57ead079844916958d477d771fe4b097ece1146988366611a0c24f0fb58b981a

Observation 10b4198b-0cec-43b7-a1c6-30f27dd6431c · outbound

This paper cites Codef: Content deformation fields for temporally consistent video processing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Codef: Content deformation fields for temporally consistent video processing

Reference 75

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Observation 44ee8ba3-8582-4b60-a7ee-57547a48c68b · outbound

This paper cites St-adapter: Parameter-efficient image-to-video transfer learning.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning St-adapter: Parameter-efficient image-to-video transfer learning

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source=pdf_text observed=2026-08-11T23:53:47.216194Z digest=sha256:0d6d8e60480ae348d548e2d0c759e5ec9af1cd7b2eaaab78609fd90b3e67b5e5

Observation 312621ee-a4a4-4af8-9f9a-92b1faed0575 · outbound

This paper cites A benchmark dataset and evaluation methodology for video object segmentation.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning A benchmark dataset and evaluation methodology for video object segmentation

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Observation 9ee919d9-4b38-4e12-94c9-6099734cfccf · outbound

This paper cites Fatezero: Fus- ing attentions for zero-shot text-based video editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Fatezero: Fus- ing attentions for zero-shot text-based video editing

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source=pdf_text observed=2026-08-11T23:53:47.227188Z digest=sha256:d851f2995148b48c8cb34c1838087947e888ef44f9d6f4b9c04d50ec5195da89

Observation 226fd1ed-3a37-45b5-84a2-5c44ddf14922 · outbound

This paper cites InstructVid2Vid: Controllable Video Editing with Natural Language Instructions.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning InstructVid2Vid: Controllable Video Editing with Natural Language Instructions

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source=pdf_text observed=2026-08-11T23:53:47.232354Z digest=sha256:ddc063f09f37ed74244d4a3621527b4b2d435cdecd770d1a60f083964387f35c

Observation 0a8542a8-7830-43ad-889c-0d9797d3be44 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Learn- ing transferable visual models from natural language super- vision

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source=pdf_text observed=2026-08-11T23:53:47.237539Z digest=sha256:3dba464c59c03e4efec4808ab7c3ba48c7f902cc7f42faf47d8428e0bc78bb70

Observation 4e383140-f51e-41f0-86ae-a34b9291f154 · outbound

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

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 81

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source=pdf_text observed=2026-08-11T23:53:47.243103Z digest=sha256:330d8a9ade4f706c00daee857497053f4524d59243f430737efea7d030bd9051

Observation 60b3b700-ff8c-47c2-b40e-212f05e44608 · outbound

This paper cites Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models

Reference 82

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source=pdf_text observed=2026-08-11T23:53:47.248410Z digest=sha256:f24444565639c65f31a2d706c4170ccb7f62431ee9cc9921833ecb0e48a66414

Observation 3b97d17e-7f7d-4c07-a21a-4a5266faaa11 · outbound

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

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning High-resolution image synthesis with latent diffusion models

Reference 83

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source=pdf_text observed=2026-08-11T23:53:47.254171Z digest=sha256:fd28652da0fbfcd5ce298af17bce32d674e4844086e877ae2ac4c4139b196afd

Observation b779f266-3c0d-4205-815b-70cfe66eab80 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning U- net: Convolutional networks for biomedical image segmen- tation

Reference 84

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source=pdf_text observed=2026-08-11T23:53:47.259426Z digest=sha256:b34d1af7b10717f3bd138a99b6cc10ca134b144879d7d22f2130a9905c7d70ae

Observation a5198b4a-e0c2-48a5-b818-dd7388d9154e · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Photorealistic text-to-image diffusion models with deep language understanding

Reference 85

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source=pdf_text observed=2026-08-11T23:53:47.264449Z digest=sha256:aff011d27694888fb8af1ee33eb9a5635673d8be3f34fdcf6f732830e0e630ef

Observation 226f5d0e-1969-4c75-92ab-550921f95b72 · outbound

This paper cites In- stantbooth: Personalized text-to-image generation without test-time finetuning.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning In- stantbooth: Personalized text-to-image generation without test-time finetuning

Reference 86

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source=pdf_text observed=2026-08-11T23:53:47.269907Z digest=sha256:ce0640b60ff448d305485ff1ef05501a95eaecb0fb77140d8436a6a1fa2b7357

Observation c3679b46-060b-43f8-9b3b-7e3127ec8061 · outbound

This paper cites Edit-a-video: Single video edit- ing with object-aware consistency.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Edit-a-video: Single video edit- ing with object-aware consistency

Reference 87

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source=pdf_text observed=2026-08-11T23:53:47.275957Z digest=sha256:0978aa608f28f380719e32d5dcfedbfaef2b507192a6b296b4ed096b4dc122e0

Observation b7b58eab-0526-4adb-b2b7-3bee6ccad1a5 · outbound

This paper cites Video Editing via Factorized Diffusion Distillation.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Video Editing via Factorized Diffusion Distillation

Reference 88

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T23:53:47.281981Z digest=sha256:e0f10279232c7322600592a4d0499c7e47fceab76120ca580f03ed0e5aca4fe2

Observation 5c64c222-d396-41b2-aeb8-4915e2ce9125 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Deep unsupervised learning using nonequilibrium thermodynamics

Reference 89

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source=pdf_text observed=2026-08-11T23:53:47.287537Z digest=sha256:c8b454bcb176e158f681ed67f65b96a1f391e4c7ee346591e0e3347a4d9fb8f8

Observation 1e80aee1-e2ca-4bfc-8b74-5eb2711f7f4c · outbound

This paper cites SAVE: Protagonist Diversification with Structure Agnostic Video Editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning SAVE: Protagonist Diversification with Structure Agnostic Video Editing

Reference 90

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T23:53:47.294656Z digest=sha256:1543027fa61c24a37e8b6dc7927f26271e2dbb5dcdc088e50f645828c7f262ad

Observation ef512c12-8a14-43c5-bd6a-dee39e9f6fb8 · outbound

This paper cites Diffusion Model-Based Video Editing: A Survey.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Diffusion Model-Based Video Editing: A Survey

Reference 91

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source=pdf_text observed=2026-08-11T23:53:47.300757Z digest=sha256:fb225361cb31b481162008c06e98aa8f620e56ccce4b66b49ffbdffcd7e88383

Observation 39838843-9cfc-4e72-a4fa-8f5b418705b8 · outbound

This paper cites Motioneditor: Editing video motion via content-aware diffusion.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Motioneditor: Editing video motion via content-aware diffusion

Reference 92

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source=pdf_text observed=2026-08-11T23:53:47.307325Z digest=sha256:bcd4d203d5a0a739f24c542df375248729fac9bea98d73a3e316425944956490

Observation fcd1329c-3743-4e56-b756-aaf6e70cb01c · outbound

This paper cites COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing

Reference 93

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source=pdf_text observed=2026-08-11T23:53:47.313966Z digest=sha256:bfdfdb76efed7d80e06dac09167968ead9df3b95a15b3949461a03a698a9e2c1

Observation 0ae848bf-22f7-4135-afb5-55b1c76d5be9 · outbound

This paper cites Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models

Reference 94

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source=pdf_text observed=2026-08-11T23:53:47.319851Z digest=sha256:f44f56cb5205494bbafad22b2a0ca0f928a9c0b9633851b9f016b717f3c99930

Observation ff1628f4-3739-442a-9d97-59dad0c254d5 · outbound

This paper cites Videocomposer: Compositional video syn- thesis with motion controllability.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Videocomposer: Compositional video syn- thesis with motion controllability

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source=pdf_text observed=2026-08-11T23:53:47.326163Z digest=sha256:50cb149cc5505eb4922a6ed5bb5aca2e9c0a1b5bdced27601c0d21bd39490b22

Observation b548fb18-73b2-4638-b42d-f8d515732f16 · outbound

This paper cites Emu3: Next-Token Prediction is All You Need.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Emu3: Next-Token Prediction is All You Need

Reference 96

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source=pdf_text observed=2026-08-11T23:53:47.331360Z digest=sha256:639a7e011cb91b7e84f7c8f4802c2d3e04c2e96b8f3cad8f8a69f0aef1982bed

Observation 8da0b682-b259-4440-a692-4e4c8aeae9f0 · outbound

This paper cites InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

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source=pdf_text observed=2026-08-11T23:53:47.337251Z digest=sha256:be68787f4e62ef3f907bda82fa117b0b73012086cba2161af4c7a794f7ac0864

Observation 11f5288f-f75c-4630-9941-7206eb1bcf94 · outbound

This paper cites Fairy: Fast parallelized instruction- guided video-to-video synthesis.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Fairy: Fast parallelized instruction- guided video-to-video synthesis

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source=pdf_text observed=2026-08-11T23:53:47.343184Z digest=sha256:b32b612d87767649b6e94bd588a2964248237e4fceda733c07839edb781b6d81

Observation f1dd2e93-18b7-452a-9890-f66ab5049873 · outbound

This paper cites Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

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source=pdf_text observed=2026-08-11T23:53:47.348490Z digest=sha256:493139366e3b38e97e8bffc4af331358c30197ac433e4fa0264275e3f43006f3

Observation be1417e2-6a52-4de8-b30b-9a100e45f107 · outbound

This paper cites Cvpr 2023 text guided video editing competition, 2023.

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning Cvpr 2023 text guided video editing competition, 2023

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source=pdf_text observed=2026-08-11T23:53:47.353461Z digest=sha256:cfc72788265928828c3c260c6091fe1a384b0290355f9965003f691d7b1f82aa

Pith citing papers

Observation eaca3b41-4ed7-44b1-9c93-2e0052ded92a · inbound

FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance cites this paper.

FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning

Reference 10

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source=pdf_text observed=2026-08-15T20:19:03.421561Z digest=sha256:e0e11fb3ad6e91fbb9a1ad155da81ea10b91bf56f6dfa2f93adbd8285e0eec79

Observation a7cab711-dbdb-42d1-b728-89ee17489c6d · inbound

Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation cites this paper.

Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning

Reference 12

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source=pdf_text observed=2026-08-05T20:20:19.757644Z digest=sha256:18f001c24f6aad12aa882408d722ec4dc08db17e5e147999901d0c3e6ec55687

Observation 651e0262-16d9-4d84-8118-b80bcf027212 · inbound

From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms cites this paper.

From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning

Reference 12

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-05T20:17:44.352960Z digest=sha256:f2da9adec0d625aa5a0cab9ce3fe5586e5cd10c2d9cb4f3be2fe435aac8b6ce1