Paper Citation Record · LEDGER

From Image to Video: An Empirical Study of Diffusion Representations

As of 9 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 2 inbound Pith citation observations for arXiv:2502.07001.

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

pith.paper-citation-record.v1
2502.07001 v2

Coverage vector

measured 69 of 69 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

69 of 69 outbound references displayed

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External citation measurements

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Outbound references

Observation 603821a0-9355-48c5-9aeb-a0481d66b0c7 · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

From Image to Video: An Empirical Study of Diffusion Representations Self-supervised learning from images with a joint-embedding predictive architecture

Reference 1

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Observation cf7f64f8-25c1-4c34-910c-da56a3c62b98 · outbound

This paper cites Video diffusion models learn the struc- ture of the dynamic world.

From Image to Video: An Empirical Study of Diffusion Representations Video diffusion models learn the struc- ture of the dynamic world

Reference 2

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Observation 84998fb5-9c41-4862-ad36-25ae68f7595f · outbound

This paper cites Learning by Reconstruction Produces Uninformative Features For Perception.

From Image to Video: An Empirical Study of Diffusion Representations Learning by Reconstruction Produces Uninformative Features For Perception

Reference 3

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This paper cites Label-efficient se- mantic segmentation with diffusion models.

From Image to Video: An Empirical Study of Diffusion Representations Label-efficient se- mantic segmentation with diffusion models

Reference 4

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Observation a21fbcaf-e472-4e30-a2ab-a570cd1ceb2a · outbound

This paper cites Revisiting Feature Prediction for Learning Visual Representations from Video.

From Image to Video: An Empirical Study of Diffusion Representations Revisiting Feature Prediction for Learning Visual Representations from Video

Reference 5

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Observation 65321793-a5fe-4987-8f30-8828dc9fc8d7 · outbound

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

From Image to Video: An Empirical Study of Diffusion Representations Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 6

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Observation ae5814d3-b1c7-4258-a241-1b50a0120411 · outbound

This paper cites Deep regression on manifolds: a 3D rota- tion case study.

From Image to Video: An Empirical Study of Diffusion Representations Deep regression on manifolds: a 3D rota- tion case study

Reference 7

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Observation 2b88fef2-7f80-42e0-b5a4-fc00d09576d3 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

From Image to Video: An Empirical Study of Diffusion Representations Emerg- ing properties in self-supervised vision transformers

Reference 8

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This paper cites Quo vadis, action recognition? a new model and the kinetics dataset.

From Image to Video: An Empirical Study of Diffusion Representations Quo vadis, action recognition? a new model and the kinetics dataset

Reference 9

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This paper cites A Short Note on the Kinetics-700 Human Action Dataset.

From Image to Video: An Empirical Study of Diffusion Representations A Short Note on the Kinetics-700 Human Action Dataset

Reference 10

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This paper cites Scaling 4D Representations.

From Image to Video: An Empirical Study of Diffusion Representations Scaling 4D Representations

Reference 11

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From Image to Video: An Empirical Study of Diffusion Representations Unresolved cited work

Reference 12

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This paper cites PaLI-3 Vision Language Models: Smaller, Faster, Stronger.

From Image to Video: An Empirical Study of Diffusion Representations PaLI-3 Vision Language Models: Smaller, Faster, Stronger

Reference 13

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This paper cites Text-to-image diffusion mod- els are zero shot classifiers.

From Image to Video: An Empirical Study of Diffusion Representations Text-to-image diffusion mod- els are zero shot classifiers

Reference 14

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This paper cites Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner.

From Image to Video: An Empirical Study of Diffusion Representations Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner

Reference 15

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Observation ceb7d32a-da1d-453d-bd88-0f0e747d5aa5 · outbound

This paper cites Depth map prediction from a single image using a multi-scale deep net- work.

From Image to Video: An Empirical Study of Diffusion Representations Depth map prediction from a single image using a multi-scale deep net- work

Reference 16

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Observation a71f2f6c-c156-4b51-a343-bf48d93d0515 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

From Image to Video: An Empirical Study of Diffusion Representations Taming transformers for high-resolution image synthesis

Reference 17

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This paper cites Masked autoencoders as spatiotemporal learners.

From Image to Video: An Empirical Study of Diffusion Representations Masked autoencoders as spatiotemporal learners

Reference 18

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Observation ade7fd2f-118a-4d8b-a295-b245f66e7678 · outbound

This paper cites Diffusion Models and Representation Learning: A Survey.

From Image to Video: An Empirical Study of Diffusion Representations Diffusion Models and Representation Learning: A Survey

Reference 19

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From Image to Video: An Empirical Study of Diffusion Representations Something Something

Reference 20

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This paper cites Kubric: A scalable dataset generator.

From Image to Video: An Empirical Study of Diffusion Representations Kubric: A scalable dataset generator

Reference 21

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This paper cites Photorealistic video generation with diffusion models.

From Image to Video: An Empirical Study of Diffusion Representations Photorealistic video generation with diffusion models

Reference 22

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Observation 7302ca0f-4509-4216-a41e-7b1fd9d63e17 · outbound

This paper cites Masked autoencoders are scalable vision learners.

From Image to Video: An Empirical Study of Diffusion Representations Masked autoencoders are scalable vision learners

Reference 23

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This paper cites Unsupervised keypoints from pretrained diffusion models.

From Image to Video: An Empirical Study of Diffusion Representations Unsupervised keypoints from pretrained diffusion models

Reference 24

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This paper cites Denoising diffu- sion probabilistic models.

From Image to Video: An Empirical Study of Diffusion Representations Denoising diffu- sion probabilistic models

Reference 25

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This paper cites DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos.

From Image to Video: An Empirical Study of Diffusion Representations DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos

Reference 26

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This paper cites Elucidating the design space of diffusion-based generative models.

From Image to Video: An Empirical Study of Diffusion Representations Elucidating the design space of diffusion-based generative models

Reference 27

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This paper cites Your diffusion model is secretly a zero-shot classifier.

From Image to Video: An Empirical Study of Diffusion Representations Your diffusion model is secretly a zero-shot classifier

Reference 28

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This paper cites Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models.

From Image to Video: An Empirical Study of Diffusion Representations Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Reference 29

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This paper cites Diffusion hyperfeatures: Search- ing through time and space for semantic correspondence.

From Image to Video: An Empirical Study of Diffusion Representations Diffusion hyperfeatures: Search- ing through time and space for semantic correspondence

Reference 30

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This paper cites Understanding deep image representations by inverting them.

From Image to Video: An Empirical Study of Diffusion Representations Understanding deep image representations by inverting them

Reference 31

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This paper cites Lexicon3d: Probing vi- sual foundation models for complex 3d scene understanding.

From Image to Video: An Empirical Study of Diffusion Representations Lexicon3d: Probing vi- sual foundation models for complex 3d scene understanding

Reference 32

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This paper cites NeRF: Representing scenes as neural radiance fields for view syn- thesis.

From Image to Video: An Empirical Study of Diffusion Representations NeRF: Representing scenes as neural radiance fields for view syn- thesis

Reference 33

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This paper cites Diffusion Models Beat GANs on Image Classification.

From Image to Video: An Empirical Study of Diffusion Representations Diffusion Models Beat GANs on Image Classification

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From Image to Video: An Empirical Study of Diffusion Representations DiffTAD: Temporal action detection with proposal denoising diffusion

Reference 35

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From Image to Video: An Empirical Study of Diffusion Representations Unresolved cited work

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From Image to Video: An Empirical Study of Diffusion Representations Self-supervised video pretraining yields robust and more human-aligned visual representations

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This paper cites Per- ception Test: A diagnostic benchmark for multimodal video models.

From Image to Video: An Empirical Study of Diffusion Representations Per- ception Test: A diagnostic benchmark for multimodal video models

Reference 38

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From Image to Video: An Empirical Study of Diffusion Representations Scalable diffusion models with transformers

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From Image to Video: An Empirical Study of Diffusion Representations The 2017 DAVIS Challenge on Video Object Segmentation

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From Image to Video: An Empirical Study of Diffusion Representations Learn- ing transferable visual models from natural language super- vision

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From Image to Video: An Empirical Study of Diffusion Representations High-resolution image syn- thesis with latent diffusion models

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From Image to Video: An Empirical Study of Diffusion Representations U- Net: Convolutional networks for biomedical image segmen- tation

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From Image to Video: An Empirical Study of Diffusion Representations Berg, and Li Fei-Fei

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From Image to Video: An Empirical Study of Diffusion Representations Scene representation transformer: Geometry-free novel view syn- thesis through set-latent scene representations

Reference 45

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This paper cites Only time can tell: Discovering temporal data for temporal modeling.

From Image to Video: An Empirical Study of Diffusion Representations Only time can tell: Discovering temporal data for temporal modeling

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This paper cites MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model.

From Image to Video: An Empirical Study of Diffusion Representations MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

From Image to Video: An Empirical Study of Diffusion Representations Deep unsupervised learning using nonequilibrium thermodynamics

Reference 48

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This paper cites Scalability in perception for autonomous driving: Waymo Open Dataset.

From Image to Video: An Empirical Study of Diffusion Representations Scalability in perception for autonomous driving: Waymo Open Dataset

Reference 49

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This paper cites Emergent correspondence from image diffusion.

From Image to Video: An Empirical Study of Diffusion Representations Emergent correspondence from image diffusion

Reference 50

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From Image to Video: An Empirical Study of Diffusion Representations VideoMAE: Masked autoencoders are data-efficient learners for self-supervised video pre-training

Reference 51

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This paper cites Towards Accurate Generative Models of Video: A New Metric & Challenges.

From Image to Video: An Empirical Study of Diffusion Representations Towards Accurate Generative Models of Video: A New Metric & Challenges

Reference 52

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From Image to Video: An Empirical Study of Diffusion Representations Neural discrete representation learning

Reference 53

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Observation 958500c9-b3bb-4d5f-9c02-e736c5693d7a · outbound

This paper cites The iNaturalist species classification and detection dataset.

From Image to Video: An Empirical Study of Diffusion Representations The iNaturalist species classification and detection dataset

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:10:55.938315Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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This paper cites Hudson, Thomas Albert Keck, Joao Carreira, Alexey Doso- vitskiy, Mehdi S.

From Image to Video: An Empirical Study of Diffusion Representations Hudson, Thomas Albert Keck, Joao Carreira, Alexey Doso- vitskiy, Mehdi S

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:10:55.923033Z

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Observation 3c76d85f-a3a4-41f4-b74a-885ad9d09450 · outbound

This paper cites Attention is all you need.

From Image to Video: An Empirical Study of Diffusion Representations Attention is all you need

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:10:55.907171Z

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Observation 7502c86a-a9a9-438b-82d0-d8af2e1c7a85 · outbound

This paper cites VideoMAE v2: Scaling video masked autoencoders with dual masking.

From Image to Video: An Empirical Study of Diffusion Representations VideoMAE v2: Scaling video masked autoencoders with dual masking

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:10:55.891728Z

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Observation 2f9ede65-af59-4efc-aee6-1dbd688e7143 · outbound

This paper cites Controlling Space and Time with Diffusion Models.

From Image to Video: An Empirical Study of Diffusion Representations Controlling Space and Time with Diffusion Models

Reference 58

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Observation 0fe87e0a-d124-4519-87a1-eba6c744e2ac · outbound

This paper cites Denoising diffusion autoencoders are unified self-supervised learners.

From Image to Video: An Empirical Study of Diffusion Representations Denoising diffusion autoencoders are unified self-supervised learners

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:10:55.874562Z

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Observation a6a4e390-b173-415c-9063-ac78ec4bac71 · outbound

This paper cites Open-vocabulary panop- tic segmentation with text-to-image diffusion models.

From Image to Video: An Empirical Study of Diffusion Representations Open-vocabulary panop- tic segmentation with text-to-image diffusion models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:10:55.858262Z

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Observation 27edcb1a-eb8b-4d72-8352-ff598a435d7b · outbound

This paper cites Diffusion Model as Rep- resentation Learner.

From Image to Video: An Empirical Study of Diffusion Representations Diffusion Model as Rep- resentation Learner

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:10:55.842384Z

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Observation b0ac0337-c4a8-47d4-ba5a-16d07178cb95 · outbound

This paper cites Gundavarapu, Luca Ver- sari, Kihyuk Sohn, David Minnen, Yong Cheng, Vigh- nesh Birodkar, Agrim Gupta, Xiuye Gu, Alexander G.

From Image to Video: An Empirical Study of Diffusion Representations Gundavarapu, Luca Ver- sari, Kihyuk Sohn, David Minnen, Yong Cheng, Vigh- nesh Birodkar, Agrim Gupta, Xiuye Gu, Alexander G

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:10:55.826303Z

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Observation af5a3197-f2c1-4c5a-901f-b84be0514da7 · outbound

This paper cites A tale of two features: Stable diffusion complements DINO for zero-shot semantic correspondence.

From Image to Video: An Empirical Study of Diffusion Representations A tale of two features: Stable diffusion complements DINO for zero-shot semantic correspondence

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:10:55.809804Z

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Observation adc973b1-8a9f-4c9f-810d-c9d4f59e998a · outbound

This paper cites A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions.

From Image to Video: An Empirical Study of Diffusion Representations A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-08T14:10:55.431225Z

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Observation 54c34bff-d2ce-477f-8bfd-a6ac1a5c169a · outbound

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From Image to Video: An Empirical Study of Diffusion Representations Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:10:55.793939Z

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Observation 1462df58-ed28-4474-a511-dcab881afb14 · outbound

This paper cites Unleashing text-to-image diffusion models for visual perception.

From Image to Video: An Empirical Study of Diffusion Representations Unleashing text-to-image diffusion models for visual perception

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-08T14:10:55.441538Z

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Observation 0f76226c-d04b-4335-801b-81acca099df6 · outbound

This paper cites Places: A 10 million image database for scene recognition.

From Image to Video: An Empirical Study of Diffusion Representations Places: A 10 million image database for scene recognition

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:10:55.767105Z

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Observation 3b551034-2701-48cf-a3d5-164bf2b1f3d6 · outbound

This paper cites Stereo magnification: Learning view syn- thesis using multiplane images.

From Image to Video: An Empirical Study of Diffusion Representations Stereo magnification: Learning view syn- thesis using multiplane images

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:10:55.750105Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6e333442-b790-49a6-871e-4f4bcf9c06e7 · outbound

This paper cites Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation.

From Image to Video: An Empirical Study of Diffusion Representations Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-08T14:10:55.455485Z

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Pith citing papers

Observation a2d5ddbd-90f3-4119-9328-a330dd5eaf9c · inbound

Frozen Forecasting: A Unified Evaluation cites this paper.

Frozen Forecasting: A Unified Evaluation From Image to Video: An Empirical Study of Diffusion Representations

Reference 41

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Observation 0b36fdbc-3921-4f57-8d81-c211e5d371ff · inbound

Video Generation with Predictive Latents cites this paper.

Video Generation with Predictive Latents From Image to Video: An Empirical Study of Diffusion Representations

Reference 48

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