Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation | Paper Citation Record

Observation eb73e75e-f5a7-4e8a-944c-359a4fe540b2 · outbound

This paper cites Memory aware synapses: Learning what (not) to forget.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Memory aware synapses: Learning what (not) to forget

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This paper cites Wasserstein gan, 2017.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Wasserstein gan, 2017

Reference 2

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Align your latents: High-resolution video synthesis with latent diffusion models

Reference 4

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This paper cites Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom

Reference 5

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This paper cites Co2l: Contrastive continual learning.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Co2l: Contrastive continual learning

Reference 6

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This paper cites 3D Spatial Understanding in MLLMs: Disambiguation and Evaluation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation 3D Spatial Understanding in MLLMs: Disambiguation and Evaluation

Reference 7

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This paper cites Mikasa: Multi-key-anchor & scene-aware transformer for 3d visual grounding.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Mikasa: Multi-key-anchor & scene-aware transformer for 3d visual grounding

Reference 8

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This paper cites Riemannian walk for incremental learning: Understanding forgetting and intransigence.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Riemannian walk for incremental learning: Understanding forgetting and intransigence

Reference 9

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This paper cites On Tiny Episodic Memories in Continual Learning.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation On Tiny Episodic Memories in Continual Learning

Reference 10

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Videocrafter2: Overcoming data limitations for high-quality video diffusion models

Reference 11

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This paper cites Gentron: Diffusion transformers for image and video generation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Gentron: Diffusion transformers for image and video generation

Reference 12

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This paper cites Can Large Language Models Be an Alternative to Human Evaluations?.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Can Large Language Models Be an Alternative to Human Evaluations?

Reference 13

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This paper cites Autoregressive Video Generation without Vector Quantization.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Autoregressive Video Generation without Vector Quantization

Reference 14

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This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Parameter-efficient fine-tuning of large-scale pre-trained language models

Reference 15

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This paper cites Carla: An open urban driving simulator.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Carla: An open urban driving simulator

Reference 16

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This paper cites Self-supervised models are continual learners.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Self-supervised models are continual learners

Reference 17

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This paper cites Vista: A generalizable driving world model with high fidelity and versatile controllability.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Vista: A generalizable driving world model with high fidelity and versatile controllability

Reference 18

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This paper cites Factorizing text-to-video generation by explicit image conditioning.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Factorizing text-to-video generation by explicit image conditioning

Reference 19

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This paper cites Generative adversarial networks.Communications of the ACM, 63(11):139– 144, 2020.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Generative adversarial networks.Communications of the ACM, 63(11):139– 144, 2020

Reference 20

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This paper cites Ego4d: Around the world in 3,000 hours of egocentric video.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Ego4d: Around the world in 3,000 hours of egocentric video

Reference 21

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This paper cites Ego-exo4d: Understanding skilled human activity from first-and third-person perspectives.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Ego-exo4d: Understanding skilled human activity from first-and third-person perspectives

Reference 22

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This paper cites GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control

Reference 23

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This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017

Reference 24

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This paper cites Denoising diffusion probabilistic models.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Denoising diffusion probabilistic models

Reference 25

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Video diffusion models

Reference 26

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 27

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This paper cites Learning a unified classifier incrementally via rebalancing.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Learning a unified classifier incrementally via rebalancing

Reference 28

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines

Reference 29

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This paper cites GAIA-1: A Generative World Model for Autonomous Driving.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation GAIA-1: A Generative World Model for Autonomous Driving

Reference 30

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation St-p3: End-to- end vision-based autonomous driving via spatial-temporal feature learning

Reference 31

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Make it move: controllable image-to-video generation with text descriptions

Reference 32

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Planning-oriented autonomous driving

Reference 33

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This paper cites $\textit{S}^3$Gaussian: Self-Supervised Street Gaussians for Autonomous Driving.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation $\textit{S}^3$Gaussian: Self-Supervised Street Gaussians for Autonomous Driving

Reference 34

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Meta-learning representations for continual learning

Reference 35

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation ADriver-I: A General World Model for Autonomous Driving

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation YOLOv11: An Overview of the Key Architectural Enhancements

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Drivegan: Towards a controllable high-quality neural simulation

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Overcoming catastrophic forgetting in neural networks

Reference 39

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Mixture of experts meets prompt-based continual learning

Reference 40

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Theory on Mixture-of-Experts in Continual Learning

Reference 41

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Unresolved cited work

Reference 42

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Flowvid: Taming imperfect optical flows for consistent video-to-video synthesis

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This paper cites Are nerfs ready for autonomous driving? towards closing the real-to-simulation gap.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Are nerfs ready for autonomous driving? towards closing the real-to-simulation gap

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Neuroncap: Photorealistic closed-loop safety testing for autonomous driving

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Wovogen: World volume-aware diffusion for controllable multi-camera driving scene generation

Reference 46

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Representational Continuity for Unsupervised Continual Learning

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This paper cites Fine-tuning can cripple your foundation model; preserving features may be the solution.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Fine-tuning can cripple your foundation model; preserving features may be the solution

Reference 48

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Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Conditional image- to-video generation with latent flow diffusion models

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This paper cites A review on deep learning techniques for video prediction.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation A review on deep learning techniques for video prediction

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This paper cites Continual lifelong learning with neural networks: A review.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Continual lifelong learning with neural networks: A review

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This paper cites Summarize the past to predict the future: Natural language descriptions of context boost multimodal object interaction anticipation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Summarize the past to predict the future: Natural language descriptions of context boost multimodal object interaction anticipation

Reference 52

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This paper cites EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation

Reference 53

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Observation 61f784c4-5cfe-431e-8e19-67207379e63d · outbound

This paper cites An outlook into the future of egocentric vision.International Journal of Computer Vision, 132(11):4880–4936, 2024.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation An outlook into the future of egocentric vision.International Journal of Computer Vision, 132(11):4880–4936, 2024

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:27.021478Z

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Observation b7d706ec-82da-4a96-a341-60b995aa72e6 · outbound

This paper cites Egovlpv2: Egocentric video-language pre-training with fusion in the backbone.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Egovlpv2: Egocentric video-language pre-training with fusion in the backbone

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:26.637213Z

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Observation 777cc87d-1801-4fe3-95d1-883cea288e99 · outbound

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation SAM 2: Segment Anything in Images and Videos

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:17.128604Z

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Observation 2b9a3e99-1518-4689-ae8d-abc517d6a2b0 · outbound

This paper cites icarl: Incremental classifier and representation learning.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation icarl: Incremental classifier and representation learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:17.263778Z

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Observation 3ab387ad-e0ec-4bce-abd9-936490fcf032 · outbound

This paper cites ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:17.468308Z

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Observation e37a25c1-206f-4005-8906-91ddcefa2e1e · outbound

This paper cites Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:17.624191Z

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Observation 55156d6d-eb82-43ca-b17f-552f4d3d8149 · outbound

This paper cites GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:17.747884Z

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Observation a68991f9-5a41-423e-8705-ee2685a82cd9 · outbound

This paper cites Gradient Projection Memory for Continual Learning.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Gradient Projection Memory for Continual Learning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:17.827340Z

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Observation d32f1260-d131-46b3-ab48-41e46cfe7376 · outbound

This paper cites Temporal generative adversarial nets with singular value clipping.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Temporal generative adversarial nets with singular value clipping

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:17.931841Z

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Observation 5c534838-df7a-49db-9fa0-b8d1ad14b7db · outbound

This paper cites Motion-i2v: Consistent and controllable image-to-video generation with explicit motion modeling.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Motion-i2v: Consistent and controllable image-to-video generation with explicit motion modeling

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:26.497988Z

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Observation 8b7d3f17-edc4-4938-9311-fa2c7a834790 · outbound

This paper cites Continual learning with deep generative replay.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Continual learning with deep generative replay

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:18.197203Z

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Observation b218cb39-08ef-4a82-bbbc-23594acb4e9f · outbound

This paper cites Ernie 2.0: A continual pre-training framework for language understanding.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Ernie 2.0: A continual pre-training framework for language understanding

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:26.312826Z

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Observation 7a85d01b-0405-4fb3-bec8-d8a3ea54c153 · outbound

This paper cites Drivingforward: Feed-forward 3d gaussian splatting for driving scene reconstruction from flexible surround-view input.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Drivingforward: Feed-forward 3d gaussian splatting for driving scene reconstruction from flexible surround-view input

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:26.140223Z

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Observation d1c49185-9cfb-43be-9241-4f8c5ccff78d · outbound

This paper cites Videotetris: Towards compositional text-to-video generation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Videotetris: Towards compositional text-to-video generation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:25.897629Z

Source-reported events for the cited work

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Observation 1f1833b2-7f08-4967-bb19-0acbf96fcd6c · outbound

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Mocogan: Decomposing motion and content for video generation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:18.732288Z

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Observation f1bef02d-7c7f-4b44-bd63-f32b5a96a1c1 · outbound

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Towards Accurate Generative Models of Video: A New Metric & Challenges

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:18.879658Z

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Observation b74c2ae6-8cb7-42e4-93f8-ebb52d66f8c5 · outbound

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Fvd: A new metric for video generation

Reference 70

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Observation 627e9b6a-3121-407b-a33e-d5d2b387a7e0 · outbound

This paper cites Three scenarios for continual learning.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Three scenarios for continual learning

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:19.195866Z

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Observation e1561fae-ebd5-4082-811c-9ea20f61ad07 · outbound

This paper cites A comprehensive survey of continual learning: Theory, method and application.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation A comprehensive survey of continual learning: Theory, method and application

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:19.352398Z

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Observation f407dbcd-9a2a-4bbd-8e3e-8a14a87cf24d · outbound

This paper cites Training networks in null space of feature covariance for continual learning.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Training networks in null space of feature covariance for continual learning

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:25.726938Z

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Observation 3034eb58-0951-4b51-b53c-fde550aa8c15 · outbound

This paper cites Swap attention in spatiotemporal diffusions for text-to-video generation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Swap attention in spatiotemporal diffusions for text-to-video generation

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:25.564846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4ce1e0a8-71a6-4ed5-944b-2dee234757b8 · outbound

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Drivedreamer: Towards real-world-drive world models for autonomous driving

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:25.356588Z

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Observation 20c1c008-1708-4410-a061-8ded1c2d7ba5 · outbound

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Emu3: Next-Token Prediction is All You Need

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:19.851789Z

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Observation e191e558-4c9a-4993-ba54-a3d2d6f2edd3 · outbound

This paper cites Lavie: High-quality video generation with cascaded latent diffusion models.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Lavie: High-quality video generation with cascaded latent diffusion models

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:25.191641Z

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Observation 81ac6b33-e529-4371-863e-a8d6aabd66ce · outbound

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:24.867699Z

Source-reported events for the cited work

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Observation 4c7c1554-2a1c-4c81-b2e3-02608da3c3b0 · outbound

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Fairy: Fast parallelized instruction-guided video-to-video synthesis

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:24.616329Z

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Observation e648002b-7a69-4abb-8ff1-b27fb60f6bd6 · outbound

This paper cites Towards A Better Metric for Text-to-Video Generation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Towards A Better Metric for Text-to-Video Generation

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:20.415679Z

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Observation b6935024-9add-4b7e-bc4d-73889601d89d · outbound

This paper cites Openemma: Open-source multimodal model for end-to-end autonomous driving.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Openemma: Open-source multimodal model for end-to-end autonomous driving

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:24.461789Z

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Observation ea4a28a0-43aa-44b2-8f68-43486ce928af · outbound

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

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation VideoGPT: Video Generation using VQ-VAE and Transformers

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:20.727575Z

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Observation 4f76bb11-7db8-44a8-ad66-a901b8c0abbc · outbound

This paper cites Street gaussians: Modeling dynamic urban scenes with gaussian splatting.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Street gaussians: Modeling dynamic urban scenes with gaussian splatting

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:24.291383Z

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Observation 6850a4f0-1f00-4d73-b0df-0e04b57a155f · outbound

This paper cites Generalized predictive model for autonomous driving.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Generalized predictive model for autonomous driving

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:24.101681Z

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Observation d05418fd-55d8-4bc4-82c7-1741339e7667 · outbound

This paper cites DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving

Reference 85

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Observation ff9539ab-2725-4e35-abae-a668e7ec7b6f · outbound

This paper cites Unisim: A neural closed-loop sensor simulator.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Unisim: A neural closed-loop sensor simulator

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:23.921877Z

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Observation bb2d40ef-d10f-43e9-a241-b7c6c3978c76 · outbound

This paper cites Continual learning through synaptic intelligence.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Continual learning through synaptic intelligence

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:21.437188Z

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Observation e6718536-ffea-46ce-9414-f0b541d54caf · outbound

This paper cites ControlVideo: Training-free Controllable Text-to-Video Generation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation ControlVideo: Training-free Controllable Text-to-Video Generation

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:21.556731Z

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Observation bd629798-cded-4631-943a-caed34b7455e · outbound

This paper cites DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:21.728429Z

Source-reported events for the cited work

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Observation 901b54ab-7195-4b96-884e-882927dc5f2a · outbound

This paper cites Learning to forecast and refine residual motion for image-to-video generation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Learning to forecast and refine residual motion for image-to-video generation

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:23.665103Z

Source-reported events for the cited work

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Observation 5f1d7869-71a0-4499-ba7f-d9a5ffdd8a3b · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:22.008587Z

Source-reported events for the cited work

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Observation 65688b26-b870-4135-98f3-f37e010b90c3 · outbound

This paper cites Drivinggaus- sian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Drivinggaus- sian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:23.366471Z

Source-reported events for the cited work

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Observation bc174b27-4004-4ffb-9aa1-2a02ea8d15b1 · outbound

This paper cites Storydiffusion: Consistent self-attention for long-range image and video generation.

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation Storydiffusion: Consistent self-attention for long-range image and video generation

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:19:23.180002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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