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Paper Citation Record · LEDGER

TransPixeler: Advancing Text-to-Video Generation with Transparency

As of 15 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2501.03006.

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

pith.paper-citation-record.v1
2501.03006 v2

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:03:13.651954Z

measured 68 of 68 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

68 of 68 outbound references displayed

  • verified exact0
  • verified fuzzy27
  • unresolved41
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 802c79ed-11f4-4261-89c1-f9c40e8215a3 · outbound

This paper cites One transformer fits all distributions in multi-modal diffu- sion at scale.

TransPixeler: Advancing Text-to-Video Generation with Transparency One transformer fits all distributions in multi-modal diffu- sion at scale

Reference 1

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Source-reported events for the cited work

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

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Observation b7601d13-fbb2-4539-8cc5-fd1a613a5774 · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

TransPixeler: Advancing Text-to-Video Generation with Transparency Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2

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source=pdf_text observed=2026-08-10T22:03:13.428470Z digest=sha256:93f1fbfc1fbe6def17680874546ef7260a3375541b2aff9ea8b5f0f3b10f8a15

Observation 629939d6-0a1d-4c3e-8059-093ec09859ee · outbound

This paper cites Video generation models as world simulators.

TransPixeler: Advancing Text-to-Video Generation with Transparency Video generation models as world simulators

Reference 3

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Observation 79175ade-57a9-4ab9-8336-9af812f98ec1 · outbound

This paper cites Magick: A large-scale captioned dataset from matting generated images using chroma keying.

TransPixeler: Advancing Text-to-Video Generation with Transparency Magick: A large-scale captioned dataset from matting generated images using chroma keying

Reference 4

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Source-reported events for the cited work

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

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Observation 44460e55-b00b-4dc2-9814-7c50ce0f1c7e · outbound

This paper cites zeroscope v2.

TransPixeler: Advancing Text-to-Video Generation with Transparency zeroscope v2

Reference 5

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Source-reported events for the cited work

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

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Observation 53addafb-f405-455f-9d7c-2be7e37aeacd · outbound

This paper cites PP-Matting: High-Accuracy Natural Image Matting.

TransPixeler: Advancing Text-to-Video Generation with Transparency PP-Matting: High-Accuracy Natural Image Matting

Reference 6

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Observation 034e862b-e019-4f0a-88d4-7a587affd2c1 · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

Reference 7

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Observation 7dd7c79c-d318-43fa-941a-28b93962ab1d · outbound

This paper cites VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models.

TransPixeler: Advancing Text-to-Video Generation with Transparency VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 8

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source=pdf_text observed=2026-08-10T22:03:13.452815Z digest=sha256:8380e658710df263f0d6be6102cd4c48f791a3ad255d81d2465f57aed62c517d

Observation 58a51d4e-9345-4b65-b35a-6255df38eaac · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

TransPixeler: Advancing Text-to-Video Generation with Transparency PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 9

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source=pdf_text observed=2026-08-10T22:03:13.457302Z digest=sha256:c215076d9a543b9fb26ab1f9131791f2a2cede4eb394e11dc7609c340844e6c3

Observation a30c5492-528c-4b5d-aaac-d095a551f953 · outbound

This paper cites ShareGPT4V: Improving Large Multi-Modal Models with Better Captions.

TransPixeler: Advancing Text-to-Video Generation with Transparency ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

Reference 10

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source=pdf_text observed=2026-08-10T22:03:13.462730Z digest=sha256:81a55eb99bbcb546cdb4345a27dd1db2b2ac506375b9c785ef57c3e04aca91ea

Observation 6fceeca7-5cc1-4d81-8ac3-98cba82c8e3d · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning

Reference 11

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source=pdf_text observed=2026-08-10T22:03:13.466003Z digest=sha256:6bdffc84652b217c73dcc5d319332cc431987c5251aac6ff37f9ef65d8ee8634

Observation 167a6211-dec9-4903-9357-17bd340a63dc · outbound

This paper cites Longnet: Scaling transformers to 1,000,000,000 tokens.

TransPixeler: Advancing Text-to-Video Generation with Transparency Longnet: Scaling transformers to 1,000,000,000 tokens

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.351711Z

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.

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Observation 6e77cf48-e743-40b7-9195-4595c0c5f3c0 · outbound

This paper cites Two-frame motion estimation based on polynomial expansion.

TransPixeler: Advancing Text-to-Video Generation with Transparency Two-frame motion estimation based on polynomial expansion

Reference 13

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raw_fallback, observed 2026-08-10T22:03:14.341940Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.475167Z digest=sha256:78b1947e2037cb729c772e3299768cf187712239ed2dd94ea40f909745f80685

Observation 628f1105-f5b1-436c-81ff-6567d904b1cc · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency TokenFlow: Consistent Diffusion Features for Consistent Video Editing

Reference 14

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Observation fd197d53-291d-47a6-82d0-6fe9a6de6704 · outbound

This paper cites LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control.

TransPixeler: Advancing Text-to-Video Generation with Transparency LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control

Reference 15

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Observation 14579c03-51cc-479a-80ad-a5641f5ab87d · outbound

This paper cites I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models.

TransPixeler: Advancing Text-to-Video Generation with Transparency I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models

Reference 16

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Observation 88b1f8a7-6b39-4b6c-aed8-6c7887d3e3ff · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 17

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Observation c3e8f7b8-b204-4140-832b-f58a943943ca · outbound

This paper cites Lu- cidfusion: Generating 3d gaussians with arbitrary unposed images, 2024.

TransPixeler: Advancing Text-to-Video Generation with Transparency Lu- cidfusion: Generating 3d gaussians with arbitrary unposed images, 2024

Reference 18

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Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.491488Z digest=sha256:a5c76e0147441039dbfe054de1fb4aa6657f799d6a0c92e3433e753609acf656

Observation 7da24728-5fa5-4ac4-bb6c-4ed251ddbe54 · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency CameraCtrl: Enabling Camera Control for Text-to-Video Generation

Reference 19

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source=pdf_text observed=2026-08-10T22:03:13.494418Z digest=sha256:d44808b5dc893974f55f20dcb0104d5ea7e668d5087d29f591d45f9c6b0d3a89

Observation 3b0a4cdd-7e3a-4945-852c-cf785ee4056f · outbound

This paper cites Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction.

TransPixeler: Advancing Text-to-Video Generation with Transparency Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction

Reference 20

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Observation 36563d41-0e49-4420-b8b2-1753fc1c840b · outbound

This paper cites Latent Video Diffusion Models for High-Fidelity Long Video Generation.

TransPixeler: Advancing Text-to-Video Generation with Transparency Latent Video Diffusion Models for High-Fidelity Long Video Generation

Reference 21

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Observation e818f96d-3bcd-4db9-95e1-3fe1ecaaf7f7 · outbound

This paper cites Denoising dif- fusion probabilistic models.

TransPixeler: Advancing Text-to-Video Generation with Transparency Denoising dif- fusion probabilistic models

Reference 22

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Observation a02183b4-8321-4586-9b97-7f256fc0ae9f · outbound

This paper cites Determining opti- cal flow.

TransPixeler: Advancing Text-to-Video Generation with Transparency Determining opti- cal flow

Reference 23

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Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.506571Z digest=sha256:b18e0fc066983e61e6270c0cb91ea410bc18f3d74a6bb5a4e3802d3a625a2559

Observation 452ce017-7d37-4721-be4e-4459bfacf3a1 · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency LoRA: Low-Rank Adaptation of Large Language Models

Reference 24

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Observation 38dd361e-257e-4160-adad-5bf8905f4452 · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency DreamMotion: Space-Time Self-Similar Score Distillation for Zero-Shot Video Editing

Reference 25

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Observation 038d4fee-4f6c-4919-8394-f41e2f340969 · outbound

This paper cites Repurpos- ing diffusion-based image generators for monocular depth estimation.

TransPixeler: Advancing Text-to-Video Generation with Transparency Repurpos- ing diffusion-based image generators for monocular depth estimation

Reference 26

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Observation f188966a-a10f-48d0-b405-4de32e1ee50a · outbound

This paper cites Open-sora-plan, 2024.

TransPixeler: Advancing Text-to-Video Generation with Transparency Open-sora-plan, 2024

Reference 27

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Source-reported events for the cited work

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

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Observation 5327516c-8149-4c04-9c1a-a909ba28ef4b · outbound

This paper cites Matting anything.

TransPixeler: Advancing Text-to-Video Generation with Transparency Matting anything

Reference 28

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Source-reported events for the cited work

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

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Observation 45d6a804-9eaf-44b9-9a22-2a894a107d0a · outbound

This paper cites Omnimat- terf: Robust omnimatte with 3d background modeling.

TransPixeler: Advancing Text-to-Video Generation with Transparency Omnimat- terf: Robust omnimatte with 3d background modeling

Reference 29

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raw_fallback, observed 2026-08-10T22:03:14.274367Z

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.

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Observation 69ad9895-e05f-4f2c-91a8-11653903f275 · outbound

This paper cites Real-time high-resolution background matting.

TransPixeler: Advancing Text-to-Video Generation with Transparency Real-time high-resolution background matting

Reference 30

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raw_fallback, observed 2026-08-10T22:03:14.262900Z

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.

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Observation edb28094-cde5-4fbe-98ba-ef51ca91fd3b · outbound

This paper cites Robust high-resolution video matting with tempo- ral guidance.

TransPixeler: Advancing Text-to-Video Generation with Transparency Robust high-resolution video matting with tempo- ral guidance

Reference 31

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raw_fallback, observed 2026-08-10T22:03:14.251951Z

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.

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Observation 5c92ac8d-3490-4e9f-8c4d-5a4a67a64fdc · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency MotionClone: Training-Free Motion Cloning for Controllable Video Generation

Reference 32

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Unavailable: canonical work link unavailable.

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Observation e77631c0-2b1c-41d1-8c84-1fe7be37657e · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency Video-p2p: Video editing with cross-attention control

Reference 33

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Observation 73b3bc10-07f2-49b6-b63d-0d20087e970a · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

TransPixeler: Advancing Text-to-Video Generation with Transparency Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 34

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Observation 1f20a71a-6e2e-49a3-aa33-8673cf478c84 · outbound

This paper cites Wonder3d: Sin- gle image to 3d using cross-domain diffusion.

TransPixeler: Advancing Text-to-Video Generation with Transparency Wonder3d: Sin- gle image to 3d using cross-domain diffusion

Reference 35

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raw_fallback, observed 2026-08-10T22:03:14.234487Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.544546Z digest=sha256:f2799a9f0394b92107619c4cbf84e240d045bded2ddc40792293135afc232494

Observation 6a6090dc-e376-45f9-936f-03ad39ddaaab · outbound

This paper cites Intrinsicdiffusion: Joint in- trinsic layers from latent diffusion models.

TransPixeler: Advancing Text-to-Video Generation with Transparency Intrinsicdiffusion: Joint in- trinsic layers from latent diffusion models

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.223573Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.548015Z digest=sha256:4f4c98f0da1eea2e6eef13cd173246ef7b23abeb13afcc3bcdc5f56c9bd01fd4

Observation 4b1f40ac-4964-4c72-a75e-24dde494b1ef · outbound

This paper cites TrailBlazer: Trajectory Control for Diffusion-Based Video Generation.

TransPixeler: Advancing Text-to-Video Generation with Transparency TrailBlazer: Trajectory Control for Diffusion-Based Video Generation

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.551286Z digest=sha256:47e6365a5c8bbdcd219c8bfd766d481cd018afc8a8232740133b8452cbc1082c

Observation 3db71a60-8289-4f83-91d0-680777d1e82d · outbound

This paper cites Latte: Latent Diffusion Transformer for Video Generation.

TransPixeler: Advancing Text-to-Video Generation with Transparency Latte: Latent Diffusion Transformer for Video Generation

Reference 38

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unresolved
no resolver link, observed 2026-08-10T22:03:13.554678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.554678Z digest=sha256:1568b1bf1be0783f693b325ef02782a72b59b36db47adbf2f86db7809478953f

Observation 0eaac291-299e-412f-9c83-c30b48190e0c · outbound

This paper cites MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model.

TransPixeler: Advancing Text-to-Video Generation with Transparency MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model

Reference 39

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unresolved
no resolver link, observed 2026-08-10T22:03:13.557527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.557527Z digest=sha256:9232da95e1a2c0168963f0c0c2134c873cddfa65979ef086e3ae32096fa59211

Observation 72746aa4-5145-4be5-907d-f8a76d3a14b6 · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency Fatezero: Fus- ing attentions for zero-shot text-based video editing

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-10T22:03:13.560797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.560797Z digest=sha256:062ea4bd39a1084074bdc7b5f593ea16e9762e072868369cf6233d618cb63af1

Observation db742f99-9932-407b-8807-fe12c9932609 · outbound

This paper cites Bimatting: Efficient video matting via binarization.

TransPixeler: Advancing Text-to-Video Generation with Transparency Bimatting: Efficient video matting via binarization

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.206501Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.564688Z digest=sha256:dda0fa561527c75798b45fee2e84827a75cd691a368846dfab4ca991f2a8d52b

Observation 64eed75c-bf0c-4969-83bb-40aaea962e3b · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

TransPixeler: Advancing Text-to-Video Generation with Transparency SAM 2: Segment Anything in Images and Videos

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-10T22:03:13.567677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.567677Z digest=sha256:1f8597fb3b1653dcda504b154d9307c9f8535be60b7cec034e2f6caee700a5e1

Observation b109daf2-2980-4743-958d-42f79c300e2f · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency High-resolution image synthesis with latent diffusion models

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-10T22:03:13.571367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.571367Z digest=sha256:ea8ae78620da87ac26fd0d7b3e223e823473493f75c27e3ef681996e8d2c311f

Observation 6b771ff2-5635-44b5-8a17-01c9229c72bc · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

TransPixeler: Advancing Text-to-Video Generation with Transparency Roformer: Enhanced transformer with rotary position embedding

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-10T22:03:13.574514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.574514Z digest=sha256:b8a1fa9c3aba2a5bd3a9a273b23fc1848a2d91f91722b1991f41dbaccf792f97

Observation 4b887705-b724-4b4f-9706-5f674216886e · outbound

This paper cites Mochi, 2024.

TransPixeler: Advancing Text-to-Video Generation with Transparency Mochi, 2024

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.182265Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.578026Z digest=sha256:7b8459e241aee447bca77b2474fda30824095e225b4f49285150e38df2277db9

Observation 2f6809d9-415c-4d7f-b01e-54c197622cf9 · outbound

This paper cites Fvd: A new metric for video generation.

TransPixeler: Advancing Text-to-Video Generation with Transparency Fvd: A new metric for video generation

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.171286Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.581084Z digest=sha256:3b6ce0a29bc28cbb8720f4cf3247350f5d17ef96fb56fb99ff60805415532f30

Observation c2980faf-133d-4ffa-b348-550d351a2b2b · outbound

This paper cites Collaborative Control for Geometry-Conditioned PBR Image Generation.

TransPixeler: Advancing Text-to-Video Generation with Transparency Collaborative Control for Geometry-Conditioned PBR Image Generation

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-10T22:03:13.584320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.584320Z digest=sha256:1d385ba10dfd3be07fefe9363f7aa2da6fd451d54d8771d7280250cdf7e5b457

Observation 6a872c3a-1c30-4301-8639-4cb92218b276 · outbound

This paper cites ModelScope Text-to-Video Technical Report.

TransPixeler: Advancing Text-to-Video Generation with Transparency ModelScope Text-to-Video Technical Report

Reference 48

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no resolver link, observed 2026-08-10T22:03:13.587944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.587944Z digest=sha256:6521b8f7b13ff7ebcca395e64d8ce2bad759246f0091caf35c9172b5005fe7b7

Observation 6b3dcc44-6f3f-4e3c-8ee4-888028d8e35b · outbound

This paper cites Motion Inversion for Video Customization.

TransPixeler: Advancing Text-to-Video Generation with Transparency Motion Inversion for Video Customization

Reference 49

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no resolver link, observed 2026-08-10T22:03:13.591248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.591248Z digest=sha256:adf809c824fde3c2ba48acd9c40b78cfff95aedc58ff9638cdebe38ae18928ff

Observation 512d08bd-f631-41c7-9d90-58751db2b9c7 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

TransPixeler: Advancing Text-to-Video Generation with Transparency Linformer: Self-Attention with Linear Complexity

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-10T22:03:13.594637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.594637Z digest=sha256:90f1a30b51d3d285c8ea96d23f976574d7de1f8fc0c52a24850af4122ffc8302

Observation 63c5cdd1-33ff-4cf2-8e4e-c9a19f0aeeb0 · outbound

This paper cites Videocomposer: Compositional video synthesis with motion controllability.

TransPixeler: Advancing Text-to-Video Generation with Transparency Videocomposer: Compositional video synthesis with motion controllability

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.159682Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.598510Z digest=sha256:7aac172627361d39484f5a61b2e7cebafe25efbedae4b18093371f0d058c2cff

Observation b6b97726-9a36-4c7b-a5d5-2d35ee45692c · outbound

This paper cites Matting by gen- eration.

TransPixeler: Advancing Text-to-Video Generation with Transparency Matting by gen- eration

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.146550Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.601790Z digest=sha256:f082fae4d6bf4ec59d28b51bc48f8683fd030c91b3234920ad7188857ee42aa9

Observation 6ad6d65a-c755-4999-8fd4-84eb1afd773d · outbound

This paper cites Motionctrl: A unified and flexible motion controller for video generation.

TransPixeler: Advancing Text-to-Video Generation with Transparency Motionctrl: A unified and flexible motion controller for video generation

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.133403Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.604754Z digest=sha256:c2720d915f5225c38ed3bb914b2646cd8ecdc0c3997cb687a8a87012d4ac6c35

Observation a2292f6f-e30b-407e-8475-aac7795b01d5 · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

Reference 54

Resolution
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no resolver link, observed 2026-08-10T22:03:13.607961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.607961Z digest=sha256:342bfd5b52937947e9f5dc6fc47fabdeda198bdb332de64c7788d08c270d6a3a

Observation cb7266e3-3d5a-4b73-be7b-5e68cfddd3a5 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

TransPixeler: Advancing Text-to-Video Generation with Transparency Depth anything: Unleashing the power of large-scale unlabeled data

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-10T22:03:13.611264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.611264Z digest=sha256:21a86c15b6ad3e24cebcc8c86b98c4575105d58904587c00c28e00f5a789067c

Observation 4d5867ce-f8bf-4556-886f-e54222dece11 · outbound

This paper cites Defect spectrum: A granular look of large-scale defect datasets with rich semantics, 2023.

TransPixeler: Advancing Text-to-Video Generation with Transparency Defect spectrum: A granular look of large-scale defect datasets with rich semantics, 2023

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.097866Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.614284Z digest=sha256:5ae447d0d98693e6ba38a8d6871a174aa6acda18318e04192cc8ab83c9483659

Observation 17a399d5-c3b3-4574-90ae-9e87fe0a46b0 · outbound

This paper cites Rerender a video: Zero-shot text-guided video-to-video translation.

TransPixeler: Advancing Text-to-Video Generation with Transparency Rerender a video: Zero-shot text-guided video-to-video translation

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.082993Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.617986Z digest=sha256:527ca162a03fca237e203a783dd3b04d36571d0ee31c7c97b089ddea87f24493

Observation 076a4e8c-9e4f-4c45-a651-b7bb78e3a649 · outbound

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

TransPixeler: Advancing Text-to-Video Generation with Transparency CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-10T22:03:13.621400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.621400Z digest=sha256:710da8fa5d151f69f52ac397a94751c133a76bf1300974577a56129943dba499

Observation fa0a8843-2384-4d79-8045-fada524902e8 · outbound

This paper cites Vitmatte: Boosting image matting with pre- trained plain vision transformers.

TransPixeler: Advancing Text-to-Video Generation with Transparency Vitmatte: Boosting image matting with pre- trained plain vision transformers

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.068558Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.625224Z digest=sha256:7b5b6a53dbf33d121a2cafe0ecd6225ae01dc72255bd66cb5fe79336f8f8de26

Observation d579d974-74f6-4ba5-bc0f-7b745600b61c · outbound

This paper cites Matte anything: Interactive natural image matting with seg- ment anything model.

TransPixeler: Advancing Text-to-Video Generation with Transparency Matte anything: Interactive natural image matting with seg- ment anything model

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.054262Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.628305Z digest=sha256:7be931699f4be3568edf8db050d7e07ab7aa70b4cada86c72d34dfebe457c5fd

Observation 85d6428e-d914-41d8-81b8-e38b830e588e · outbound

This paper cites DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory.

TransPixeler: Advancing Text-to-Video Generation with Transparency DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory

Reference 61

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unresolved
no resolver link, observed 2026-08-10T22:03:13.631543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.631543Z digest=sha256:ba7ed9b13e4194300d31493143ee415ec2d3c38db527af753ddaf43d6cabb304

Observation 1372ece5-a478-4521-9186-b3cd2980fe5c · outbound

This paper cites Rgb ↔x: Image decomposition and synthesis using material-and lighting-aware diffusion models.

TransPixeler: Advancing Text-to-Video Generation with Transparency Rgb ↔x: Image decomposition and synthesis using material-and lighting-aware diffusion models

Reference 62

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verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.038519Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.635056Z digest=sha256:7110a16ae5570da1cb5c4a15a0bd690397e05712acf1f78f661fe240f02743c0

Observation 0a911b25-bec6-4f3e-ae88-c69ad6e900f2 · outbound

This paper cites Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation.

TransPixeler: Advancing Text-to-Video Generation with Transparency Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 63

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unresolved
no resolver link, observed 2026-08-10T22:03:13.638542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.638542Z digest=sha256:af3ef22a1d6af9f0adc3d3267b42f480542de397227975e6c6a7eb3e1047dc85

Observation 51f06818-c4bc-4b66-a96d-abe6d9a07655 · outbound

This paper cites Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions.

TransPixeler: Advancing Text-to-Video Generation with Transparency Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-10T22:03:13.641920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.641920Z digest=sha256:80aca500a22deed266880931a6c79c01e2e0f66b999e3c4ca9495b01cdbef0ff

Observation 4ea32c1b-ee0a-4ca0-94af-761e78d1a6d2 · outbound

This paper cites Transparent Image Layer Diffusion using Latent Transparency.

TransPixeler: Advancing Text-to-Video Generation with Transparency Transparent Image Layer Diffusion using Latent Transparency

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-10T22:03:13.645613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.645613Z digest=sha256:d4a9de0e00ac5e1860835c54bcc289ef4fde31857b377f27b487db039b4ba925

Observation d7f1cd08-dae3-42d8-b294-952a66ebaa54 · outbound

This paper cites Open-sora: Democratizing efficient video production for all, 2024.

TransPixeler: Advancing Text-to-Video Generation with Transparency Open-sora: Democratizing efficient video production for all, 2024

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.024905Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.648867Z digest=sha256:4919a72eb1648a3e64df2965d96a75468911dadc4c2f0497bdb03b00da347def

Observation 101c528c-8c6d-47fb-8c0a-7296197812fe · outbound

This paper cites Long-short transformer: Efficient transformers for language and vision.

TransPixeler: Advancing Text-to-Video Generation with Transparency Long-short transformer: Efficient transformers for language and vision

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:03:14.007505Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-10T22:03:13.651954Z digest=sha256:dc733d2f85a58b0f8baa4bdbc4c30de6e8390bac25166e9e2e34ed91621c246f

Observation 15c9ba3e-9780-4234-80b1-162c66cc75c6 · outbound

This paper cites LongNet: Scaling Transformers to 1,000,000,000 Tokens.

TransPixeler: Advancing Text-to-Video Generation with Transparency LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-10T22:03:13.472136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:03:13.472136Z digest=sha256:b3f512026210b8e8d18367e48b14080bec310759532ac94cc6fc04ef165f53ce

Pith citing papers

No inbound Pith citation observations are available.