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

arXiv 25.8 2025 49.1 method, application

TLDR

Fine-tuned video generators for driving simulation improve visual fidelity but degrade spatial accuracy; continual learning offers a balanced alternative.

Reasoning

The paper identifies a critical trade-off in fine-tuning video generators for driving simulation, supported by analysis of driving scene regularity. Its strength lies in revealing this overlooked issue and proposing a simple solution, but it is limited to driving simulation and lacks broader validation.

Read-first score

Read-first score 49.1, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 25.

Recency 8%

86.7

Uses a gentle age decay so recent papers surface without erasing older foundations. 2025

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=dataset,result

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=dataset

Topical relevance 42%

35.7

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Field roles

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 422.

Keyword Scores

world model

generative world model

world dynamics prediction

video world model

world simulator

interactive world model

model-based reinforcement learning world model

Deep Analysis

Innovations

Identification of a trade-off between visual fidelity and spatial accuracy when fine-tuning video generators on driving datasets
Attribution of the degradation to a shift in alignment between visual quality and dynamic understanding objectives due to the regular and repetitive nature of driving scenes
Proposal of continual learning with replay from diverse domains as a balanced alternative to preserve spatial accuracy while maintaining visual quality

Methodology

The study investigates the effects of fine-tuning video generation models on structured driving datasets, analyzing the trade-off between visual fidelity and spatial accuracy. They propose using continual learning strategies, specifically replay from diverse domains, to mitigate the degradation. The methodology involves comparing fine-tuned models with those trained using continual learning on driving simulation tasks.

Key Results

Fine-tuning improves visual fidelity but degrades spatial accuracy in modeling dynamic elements. Continual learning with replay from diverse domains can preserve spatial accuracy while maintaining strong visual quality.

Limitations

Fine-tuning on regular driving scenes leads to a trade-off where spatial accuracy degrades despite improved visual fidelity
The proposed continual learning approach may require access to diverse domain data, which is not always available

Resources

Paper