Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation | Paper Citation Record

Observation 7507d4bc-2c94-4fb3-b3f0-958b30409cfa · outbound

This paper cites Logistic-normal distribu- tions: Some properties and uses.Biometrika, 67(2):261–272,.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Logistic-normal distribu- tions: Some properties and uses.Biometrika, 67(2):261–272,

Reference 1

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Observation dbced537-461b-45fd-96cb-84a87dd07eda · outbound

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

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2

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Observation ca3b3a7b-5e18-4878-bab6-8fd7039f786b · outbound

This paper cites Video generation models as world simulators.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Video generation models as world simulators

Reference 3

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This paper cites nuscenes: A multi- modal dataset for autonomous driving.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation nuscenes: A multi- modal dataset for autonomous driving

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:03.552753Z

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This paper cites NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

Reference 5

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Observation 713c5b4c-5f71-415c-85f9-a68a5ec44112 · outbound

This paper cites Diffusion forcing: Next-token prediction meets full-sequence diffu- sion.Advances in Neural Information Processing Systems, 37:24081–24125, 2024.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Diffusion forcing: Next-token prediction meets full-sequence diffu- sion.Advances in Neural Information Processing Systems, 37:24081–24125, 2024

Reference 6

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Observation 875d36ed-4801-486d-8b7f-667501cc210b · outbound

This paper cites Deep compres- sion autoencoder for efficient high-resolution diffusion mod- els.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Deep compres- sion autoencoder for efficient high-resolution diffusion mod- els

Reference 7

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Observation 311850ca-fa2e-479a-be63-795fe10dd11e · outbound

This paper cites Empower- ing world models with reflection for embodied video predic- tion.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Empower- ing world models with reflection for embodied video predic- tion

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:03.516418Z

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Observation 32dde14e-8a1c-45e6-ab7b-2e99c775244c · outbound

This paper cites Self-Forcing++: Towards Minute-Scale High-Quality Video Generation.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Self-Forcing++: Towards Minute-Scale High-Quality Video Generation

Reference 9

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Observation 734e817d-2238-4e9e-87fe-22644f72b0bd · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 10

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Observation 9dfcf6d3-a709-4501-9bd7-176361c2412f · outbound

This paper cites RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework

Reference 11

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Observation 9826d9b3-6cff-4d49-b97a-d4a3a2b57671 · outbound

This paper cites Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing

Reference 12

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Observation ba72e877-d7e5-47b0-b880-3858f12f8b06 · outbound

This paper cites Long-Context Autoregressive Video Modeling with Next-Frame Prediction.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Long-Context Autoregressive Video Modeling with Next-Frame Prediction

Reference 13

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Observation f55f291d-2160-4b87-ac01-910f76666944 · outbound

This paper cites Long Context Tuning for Video Generation.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Long Context Tuning for Video Generation

Reference 14

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This paper cites World Models.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation World Models

Reference 15

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Observation ae6d9708-26b8-4a25-bd49-981eb8c36e51 · outbound

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

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Latent Video Diffusion Models for High-Fidelity Long Video Generation

Reference 16

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This paper cites Streamingt2v: Con- sistent, dynamic, and extendable long video generation from text.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Streamingt2v: Con- sistent, dynamic, and extendable long video generation from text

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:03.497341Z

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Observation f78355c6-0dd4-4728-83e8-438b758e6e58 · outbound

This paper cites Video dif- fusion models.Advances in neural information processing systems, 35:8633–8646, 2022.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Video dif- fusion models.Advances in neural information processing systems, 35:8633–8646, 2022

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:03.487275Z

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Observation b998d856-cf35-48b0-b204-ec83b16d9e8a · outbound

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

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 19

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Observation 70cca9d1-ec2c-4532-9a99-d754ebcbbdf9 · outbound

This paper cites Acdit: Interpolating autoregressive con- ditional modeling and diffusion transformer.arXiv preprint arXiv:2412.07720, 2024.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Acdit: Interpolating autoregressive con- ditional modeling and diffusion transformer.arXiv preprint arXiv:2412.07720, 2024

Reference 20

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Observation 278d6f1e-43ca-40fe-b91d-d84d89cb43f2 · outbound

This paper cites Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion

Reference 21

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Observation 508b3066-8d1b-4522-b760-88edf79e336e · outbound

This paper cites Fifo-diffusion: Generating infinite videos from text without training.Advances in Neural Information Processing Sys- tems, 37:89834–89868, 2024.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Fifo-diffusion: Generating infinite videos from text without training.Advances in Neural Information Processing Sys- tems, 37:89834–89868, 2024

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:03.477314Z

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Observation b3cbdc72-4bdf-440c-bdae-c9499ee1c83d · outbound

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

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Hybrid video diffusion models with 2d triplane and 3d wavelet rep- resentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:03.467201Z

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Observation fbd2a5c8-cfe5-4598-880f-8e92cf2a1f4b · outbound

This paper cites Drivegan: Towards a controllable high-quality neural simulation.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Drivegan: Towards a controllable high-quality neural simulation

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Observation 0de8aaaa-05e6-4f27-b509-aa08aca4f191 · outbound

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

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Videopoet: A large language model for zero-shot video gen- eration

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:03.447298Z

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Observation 0ea2fbab-5684-478d-9206-5a98e8eb54b5 · outbound

This paper cites FrameDiT: Diffusion Transformer with Matrix Attention for Efficient Video Generation.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation FrameDiT: Diffusion Transformer with Matrix Attention for Efficient Video Generation

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Observation 3dfe1268-1b4d-4e56-8308-e43fecba378b · outbound

This paper cites MarDini: Masked Autoregressive Diffusion for Video Generation at Scale.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation MarDini: Masked Autoregressive Diffusion for Video Generation at Scale

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Observation 7d52a65e-3568-42c8-92c0-9c769a3eb5e9 · outbound

This paper cites Rolling Forcing: Autoregressive Long Video Diffusion in Real Time.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Rolling Forcing: Autoregressive Long Video Diffusion in Real Time

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Observation b52035fa-11d9-4c53-a7f1-d414ec3df3d1 · outbound

This paper cites Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach

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Observation 81eba023-2930-404d-b39f-4c66f933bc94 · outbound

This paper cites Wovogen: World volume-aware diffusion for con- trollable multi-camera driving scene generation.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Wovogen: World volume-aware diffusion for con- trollable multi-camera driving scene generation

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Observation e84d3339-c294-452c-a73f-06a08e9539e2 · outbound

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

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Latte: Latent Diffusion Transformer for Video Generation

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This paper cites Neural residual diffusion models for deep scalable vision generation.Advances in Neural Information Processing Sys- tems, 37:117456–117480, 2024.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Neural residual diffusion models for deep scalable vision generation.Advances in Neural Information Processing Sys- tems, 37:117456–117480, 2024

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Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Unresolved cited work

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Observation f79979a0-7a6a-4168-bad2-dcccd32999b9 · outbound

This paper cites Worldsimbench: Towards video generation models as world simulators.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Worldsimbench: Towards video generation models as world simulators

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Observation 87e15741-c406-4c55-9f77-d4943b9364d3 · outbound

This paper cites Rolling diffusion models.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Rolling diffusion models

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Observation 8bce7373-1827-4441-ac18-1565d8ca0753 · outbound

This paper cites Seedance 2.0: Advancing Video Generation for World Complexity.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Seedance 2.0: Advancing Video Generation for World Complexity

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This paper cites First order motion model for image animation.Advances in neural information processing systems, 32, 2019.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation First order motion model for image animation.Advances in neural information processing systems, 32, 2019

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Observation 3507b367-c34e-4803-aae0-3b7c7a45f4e7 · outbound

This paper cites Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2

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Observation 515d642c-68d0-4c47-a245-58d5cdf61076 · outbound

This paper cites History-Guided Video Diffusion.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation History-Guided Video Diffusion

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

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

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Observation 8f861e6f-24da-4610-b236-fd598462c5f5 · outbound

This paper cites Ar-diffusion: Asynchronous video genera- tion with auto-regressive diffusion.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Ar-diffusion: Asynchronous video genera- tion with auto-regressive diffusion

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This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

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This paper cites Kling-Omni Technical Report.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Kling-Omni Technical Report

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Observation fb27b2d0-5c8b-4463-a3de-aa8b231546c5 · outbound

This paper cites MAGI-1: Autoregressive Video Generation at Scale.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation MAGI-1: Autoregressive Video Generation at Scale

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Observation c7a173c4-b824-425d-a18e-3901d098b93a · outbound

This paper cites Visual autoregressive modeling: Scalable image generation via next-scale prediction.Advances in neural in- formation processing systems, 37:84839–84865, 2024.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Visual autoregressive modeling: Scalable image generation via next-scale prediction.Advances in neural in- formation processing systems, 37:84839–84865, 2024

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Observation 9ddcc877-08ff-4eb5-8b6f-35ec1e22dce1 · outbound

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

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Towards Accurate Generative Models of Video: A New Metric & Challenges

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This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Wan: Open and Advanced Large-Scale Video Generative Models

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This paper cites Drivedreamer: Towards real-world- drive world models for autonomous driving.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Drivedreamer: Towards real-world- drive world models for autonomous driving

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:03.341020Z

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Observation c84a4a7a-07da-4b0a-887e-8023696c0e9c · outbound

This paper cites Driving into the future: Multiview visual forecasting and planning with world model for au- tonomous driving.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Driving into the future: Multiview visual forecasting and planning with world model for au- tonomous driving

Reference 49

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Observation c4ca32f3-efd9-4ae0-96ae-c2f06e8ce615 · outbound

This paper cites Loong: Generating Minute-level Long Videos with Autoregressive Language Models.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Loong: Generating Minute-level Long Videos with Autoregressive Language Models

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This paper cites Art-v: Auto-regressive text-to- video generation with diffusion models.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Art-v: Auto-regressive text-to- video generation with diffusion models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:03.315534Z

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Observation 3ed7562c-a6cc-442a-9c94-dae78728772c · outbound

This paper cites DriveLaW:Unifying Planning and Video Generation in a Latent Driving World.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation DriveLaW:Unifying Planning and Video Generation in a Latent Driving World

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This paper cites Progressive au- toregressive video diffusion models.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Progressive au- toregressive video diffusion models

Reference 53

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Observation fada23ee-3a57-48e8-8d52-67554c6cf6f0 · outbound

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

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation VideoGPT: Video Generation using VQ-VAE and Transformers

Reference 54

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This paper cites ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos

Reference 55

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This paper cites Generalized predictive model for autonomous driving.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Generalized predictive model for autonomous driving

Reference 56

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This paper cites From slow bidirectional to fast autoregressive video diffusion mod- els.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation From slow bidirectional to fast autoregressive video diffusion mod- els

Reference 57

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This paper cites An image is worth 32 tokens for reconstruction and generation.Advances in Neural Information Processing Systems, 37:128940– 128966, 2024.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation An image is worth 32 tokens for reconstruction and generation.Advances in Neural Information Processing Systems, 37:128940– 128966, 2024

Reference 58

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Observation cb2c62fa-bc57-44f9-8842-e6dadd6af07f · outbound

This paper cites Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks

Reference 59

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Observation 1f44a1b4-2e9c-4e3d-8c80-073af1abc404 · outbound

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

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Video probabilistic diffusion models in projected latent space

Reference 60

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This paper cites Packing input frame context in next-frame prediction models for video genera- tion.arXiv preprint arXiv:2504.12626, 2025.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Packing input frame context in next-frame prediction models for video genera- tion.arXiv preprint arXiv:2504.12626, 2025

Reference 61

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This paper cites TinyHistory: Lightweight Video History Embeddings via Two-Stage Context Learning.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation TinyHistory: Lightweight Video History Embeddings via Two-Stage Context Learning

Reference 62

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This paper cites Mobilei2v: Fast and high-resolution image-to- video on mobile devices.arXiv preprint arXiv:2511.21475,.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Mobilei2v: Fast and high-resolution image-to- video on mobile devices.arXiv preprint arXiv:2511.21475,

Reference 63

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Observation 330d539b-d012-480e-9869-37228cae2ec7 · outbound

This paper cites Taming teacher forcing for masked autoregressive video gen- eration.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Taming teacher forcing for masked autoregressive video gen- eration

Reference 64

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Observation cbb231e2-d06f-4c63-b6d0-825dd8bf7957 · outbound

This paper cites Abrupt changes in the sampling distribution may introduce sud- den shifts in training difficulty and destabilize the cur- riculum.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Abrupt changes in the sampling distribution may introduce sud- den shifts in training difficulty and destabilize the cur- riculum

Reference 65

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This paper cites an unresolved cited work.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation Unresolved cited work

Reference 66

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Observation cb06a336-df76-4c5b-848b-c48a26b33a11 · outbound

This paper cites In contrast, inference follows an or- dered denoising process in which the noise levels of ad- jacent frames are strongly correlated.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation In contrast, inference follows an or- dered denoising process in which the noise levels of ad- jacent frames are strongly correlated

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:03.221138Z

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Observation 6f60e16a-1fba-4e4a-88ce-10b0a1203c3c · outbound

This paper cites All methods generate videos at 256×256 resolution and 16 frames.

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation All methods generate videos at 256×256 resolution and 16 frames

Reference 101

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