Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction | Paper Citation Record

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

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Align your latents: High-resolution video synthesis with la- tent diffusion models

Reference 1

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Observation f75b73ef-49af-4791-8269-8e9a2363f1b3 · outbound

This paper cites Video generation models as world simulators.Ope- nAI Blog, 2024.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Video generation models as world simulators.Ope- nAI Blog, 2024

Reference 2

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

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Quo vadis, action recognition? a new model and the kinetics dataset

Reference 3

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This paper cites Efficient geometry-aware 3d generative adversarial networks.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Efficient geometry-aware 3d generative adversarial networks

Reference 4

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This paper cites MaskGIT: Masked generative image transformer.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction MaskGIT: Masked generative image transformer

Reference 5

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This paper cites VideoINR: Learning video implicit neural represen- tation for continuous space-time super-resolution.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction VideoINR: Learning video implicit neural represen- tation for continuous space-time super-resolution

Reference 6

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This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 7

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

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Taming transformers for high-resolution image synthesis

Reference 8

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Observation f3793d6f-e1eb-4587-aa6c-6d9df4e143f2 · outbound

This paper cites Cosmos world foundation model platform for physical ai.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Cosmos world foundation model platform for physical ai

Reference 9

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This paper cites Latte: Latent diffusion transformer for video generation.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Latte: Latent diffusion transformer for video generation

Reference 10

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This paper cites Long video generation with time-agnostic vqgan and time- sensitive transformer.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Long video generation with time-agnostic vqgan and time- sensitive transformer

Reference 11

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This paper cites Generative adversarial nets.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Generative adversarial nets

Reference 12

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This paper cites Photorealistic video generation with diffusion models.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Photorealistic video generation with diffusion models

Reference 13

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This paper cites A technical overview of av1.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction A technical overview of av1

Reference 14

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

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Latent Video Diffusion Models for High-Fidelity Long Video Generation

Reference 15

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This paper cites Denoising dif- fusion probabilistic models.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Denoising dif- fusion probabilistic models

Reference 16

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This paper cites CogVideo: Large-scale pretraining for text-to- video generation via transformers.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction CogVideo: Large-scale pretraining for text-to- video generation via transformers

Reference 17

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This paper cites LRM: Large reconstruction model for single image to 3D.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction LRM: Large reconstruction model for single image to 3D

Reference 18

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Observation ccc72d19-cf63-4fcd-a704-37c7873e9d70 · outbound

This paper cites Shap-E: Generating Conditional 3D Implicit Functions.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Shap-E: Generating Conditional 3D Implicit Functions

Reference 19

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This paper cites Analyzing and improving the image quality of stylegan.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Analyzing and improving the image quality of stylegan

Reference 20

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This paper cites Hybrid video diffusion models with 2d triplane and 3d wavelet rep- resentation.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Hybrid video diffusion models with 2d triplane and 3d wavelet rep- resentation

Reference 21

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This paper cites Scal- able neural video representations with learnable positional features.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Scal- able neural video representations with learnable positional features

Reference 22

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This paper cites 9 Videopoet: A large language model for zero-shot video gen- eration.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction 9 Videopoet: A large language model for zero-shot video gen- eration

Reference 23

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This paper cites One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimiza- tion.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimiza- tion

Reference 24

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Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Decoupled weight de- cay regularization

Reference 25

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This paper cites Vdt: General-purpose video diffusion transformers via mask modeling.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Vdt: General-purpose video diffusion transformers via mask modeling

Reference 26

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This paper cites SiT: Explor- ing flow and diffusion-based generative models with scalable interpolant transformers.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction SiT: Explor- ing flow and diffusion-based generative models with scalable interpolant transformers

Reference 27

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Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Unresolved cited work

Reference 28

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This paper cites GTA: A geometry-aware attention mechanism for multi-view transformers.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction GTA: A geometry-aware attention mechanism for multi-view transformers

Reference 29

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This paper cites A technical overview of vp9—the latest open- source video codec.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction A technical overview of vp9—the latest open- source video codec

Reference 30

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This paper cites Scalable diffusion mod- els with transformers.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Scalable diffusion mod- els with transformers

Reference 31

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This paper cites Searching for Activation Functions.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Searching for Activation Functions

Reference 32

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This paper cites Gen- erating diverse high-fidelity images with VQ-V AE-2.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Gen- erating diverse high-fidelity images with VQ-V AE-2

Reference 33

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Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Unresolved cited work

Reference 34

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

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction High-resolution image syn- thesis with latent diffusion models

Reference 35

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This paper cites Very deep convolutional networks for large-scale image recognition.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Very deep convolutional networks for large-scale image recognition

Reference 36

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This paper cites Make-a-video: Text-to-video genera- tion without text-video data.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Make-a-video: Text-to-video genera- tion without text-video data

Reference 37

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This paper cites Implicit neural representa- tions with periodic activation functions.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Implicit neural representa- tions with periodic activation functions

Reference 38

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This paper cites StyleGAN-V: A continuous video generator with the price, image quality and perks of StyleGAN2.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction StyleGAN-V: A continuous video generator with the price, image quality and perks of StyleGAN2

Reference 39

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Deep unsupervised learning using nonequilibrium thermodynamics

Reference 40

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This paper cites Denois- ing diffusion implicit models.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Denois- ing diffusion implicit models

Reference 41

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This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 42

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This paper cites Overview of the high efficiency video coding (hevc) standard.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Overview of the high efficiency video coding (hevc) standard

Reference 43

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This paper cites High efficiency video coding (hevc).

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction High efficiency video coding (hevc)

Reference 44

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Observation cb39484a-e85c-428f-8d84-f58fea5c2a79 · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 45

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This paper cites A good image generator is what you need for high-resolution video synthe- sis.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction A good image generator is what you need for high-resolution video synthe- sis

Reference 46

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This paper cites MoCoGAN: Decomposing motion and content for video generation.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction MoCoGAN: Decomposing motion and content for video generation

Reference 47

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This paper cites FVD: A new metric for video generation, 2019.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction FVD: A new metric for video generation, 2019

Reference 48

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This paper cites Neural discrete representation learning.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Neural discrete representation learning

Reference 49

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This paper cites Attention is all you need.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Attention is all you need

Reference 50

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This paper cites Phenaki: Variable length video generation from open domain textual descriptions.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Phenaki: Variable length video generation from open domain textual descriptions

Reference 51

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This paper cites LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior

Reference 52

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This paper cites OmniTokenizer: A joint image- video tokenizer for visual generation.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction OmniTokenizer: A joint image- video tokenizer for visual generation

Reference 53

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Observation 2d9ba59e-87c2-4426-b397-bd9c44f16421 · outbound

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

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Emu3: Next-Token Prediction is All You Need

Reference 54

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Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Image quality assessment: from error visibility to structural similarity

Reference 55

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This paper cites Overview of the h.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Overview of the h

Reference 56

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Observation 8f20aa74-0595-41db-8739-232e8b4a8c1e · outbound

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Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Group normalization

Reference 57

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Observation 0a4afa8b-aee9-4585-8e79-e699a29ca685 · outbound

This paper cites VideoGPT: Video Generation using VQ-VAE and Transformers.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction VideoGPT: Video Generation using VQ-VAE and Transformers

Reference 58

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Observation d2082f80-eb7f-4a40-9876-f6cc0be50748 · outbound

This paper cites Temporally consistent transformers for video gen- eration.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Temporally consistent transformers for video gen- eration

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:00:32.168884Z

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Observation 47d1455c-4771-4e51-b3fa-d1ece7871991 · outbound

This paper cites ElasticTok: Adaptive Tokenization for Image and Video.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction ElasticTok: Adaptive Tokenization for Image and Video

Reference 60

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Observation 4992d67d-bb4f-46f8-b665-d215be3bcc53 · outbound

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

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 61

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Observation 1dd3f11c-d7c5-4685-83c3-505f9eec3555 · outbound

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Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Towards end-to-end generative model- ing of long videos with memory-efficient bidirectional trans- formers

Reference 62

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Observation 165bd51e-bf33-4a2c-ae8b-8b7a02a5f4fe · outbound

This paper cites Magvit: Masked generative video transformer.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Magvit: Masked generative video transformer

Reference 63

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Observation 63aef45f-b108-4b22-9938-1df582be05ac · outbound

This paper cites Language model beats diffusion–tokenizer is key to visual generation.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Language model beats diffusion–tokenizer is key to visual generation

Reference 64

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Observation 610180e5-aa84-4e69-8a43-d941f533ed4d · outbound

This paper cites Generating videos with dynamics-aware implicit generative adversarial net- works.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Generating videos with dynamics-aware implicit generative adversarial net- works

Reference 65

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Observation acdafe3f-e627-4e2f-8b1e-01db5e71de82 · outbound

This paper cites Video probabilistic diffusion models in projected latent space.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Video probabilistic diffusion models in projected latent space

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:00:32.102853Z

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Observation b29d40b4-e2b6-46a0-89e3-48e5402a8ac6 · outbound

This paper cites Efficient video diffusion models via content-frame motion-latent decomposition.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Efficient video diffusion models via content-frame motion-latent decomposition

Reference 67

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Observation a37244c2-9d80-4882-ab5b-a1cc90c788c6 · outbound

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

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction The unreasonable effectiveness of deep features as a perceptual metric

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:00:32.076180Z

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Observation 242ebf3b-1c68-4012-a82f-02b04a5cc823 · outbound

This paper cites Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 69

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Observation 6b27ceea-0e36-440c-ac8c-cd605693df84 · outbound

This paper cites MagicVideo: Efficient Video Generation With Latent Diffusion Models.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction MagicVideo: Efficient Video Generation With Latent Diffusion Models

Reference 70

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Observation 9edbdde8-17b5-41f8-8473-b1604ffba88c · outbound

This paper cites Architecture We use the same structure as SiT, except that our patch embedding and final projection layers are im- plemented separately for each plane.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Architecture We use the same structure as SiT, except that our patch embedding and final projection layers are im- plemented separately for each plane

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:00:32.061934Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4d1fa4bd-360f-484c-8f47-e2142ff03862 · outbound

This paper cites an unresolved cited work.

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:00:32.047444Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Reference resolution

Outbound references

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