Title: RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: video generation

diffusion model

autonomous driving
Abstract: Synthetic data is crucial for advancing autonomous driving (AD) systems, yet current state-of-the-art video generation models, despite their visual realism, suffer from subtle geometric distortions that limit their utility for downstream perception tasks. 
We identify and quantify this critical issue, demonstrating a significant performance gap in 3D object detection when using synthetic versus real data.
To address this, we introduce Reinforcement Learning with Geometric Feedback (RLGF), RLGF uniquely refines video diffusion models by incorporating rewards from specialized latent-space AD perception models. 
Its core components include an efficient Latent-Space Windowing Optimization technique for targeted feedback during diffusion, and a Hierarchical Geometric Reward (HGR) system providing multi-level rewards for point-line-plane alignment, and scene occupancy coherence. 
To quantify these distortions, we propose GeoScores. Applied to models like DiVE on nuScenes, RLGF substantially reduces geometric errors (e.g., VP error by 21\%, Depth error by 57\%) and dramatically improves 3D object detection mAP by 12.7\%, narrowing the gap to real-data performance. RLGF offers a plug-and-play solution for generating geometrically sound and reliable synthetic videos for AD development.

RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generation

Abstract

Synthetic data is crucial for advancing autonomous driving (AD) systems, yet current state-of-the-art video generation models, despite their visual realism, suffer from subtle geometric distortions that limit their utility for downstream perception tasks. We identify and quantify this critical issue, demonstrating a significant performance gap in 3D object detection when using synthetic versus real data. To address this, we introduce Reinforcement Learning with Geometric Feedback (RLGF), RLGF uniquely refines video diffusion models by incorporating rewards from specialized latent-space AD perception models. Its core components include an efficient Latent-Space Windowing Optimization technique for targeted feedback during diffusion, and a Hierarchical Geometric Reward (HGR) system providing multi-level rewards for point-line-plane alignment, and scene occupancy coherence. To quantify these distortions, we propose GeoScores. Applied to models like DiVE on nuScenes, RLGF substantially reduces geometric errors (e.g., VP error by 21%, Depth error by 57%) and dramatically improves 3D object detection mAP by 12.7%, narrowing the gap to real-data performance. RLGF offers a plug-and-play solution for generating geometrically sound and reliable synthetic videos for AD development.

1 Introduction

The rapid progress of autonomous driving (AD) systems [15, 17, 6] has created a growing need for high-quality synthetic data. Recent diffusion-based video generation methods [57, 61, 30, 10, 9, 18, 19] have achieved state-of-the-art visual realism, measured by metrics like FVD [49]. However, we identify a critical yet underexplored limitation: the generated videos often contain subtle yet impactful incorrect geometric relationships. This flaw not only misleads downstream perception and planning tasks but also undermines the reliability of models trained or evaluated using such data, significantly constraining their applicability in essential use cases such as simulation-based training [30] and system validation [61, 67].

To investigate this geometric limitation, we conduct a series of targeted experiments. Evaluating 3D object detection on synthetic videos using BEVFusion [26] reveals a substantial performance drop compared to real data (mAP: 25.7 vs. 35.5). In contrast, 2D object detection using YOLOv5 [20]

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on the same data yields results comparable to real-world samples (mAP: 43.8 vs. 44.7). These findings suggest that while current diffusion models [57, 56, 18, 9] preserve 2D appearance, indicating minimal image-level domain gap, yet they fail to capture accurate 3D scene structure. We attribute this primarily to underlying geometric inconsistencies. To further verify this hypothesis and systematically quantify these distortions, we introduce GeoScores. This metric suite evaluates geometric fidelity by applying pre-trained perception models [65, 42] to both synthetic videos and their corresponding real-world counterparts, using the outputs from real videos as reference ground truth. Significant discrepancies between the two highlight geometric errors. GeoScores (details are in appendix A.1) reveals three major issues: (1) Vanishing point shifts, indicating incorrect global perspective; (2) Lane topology inconsistencies, reflecting misaligned road markings and implausible lane structures; and (3) Depth errors, particularly on road surfaces, signifying incorrect placement. (e.g., for a typical baseline, an average VP shift of 0.086 normalized units, a Lane F1-Score of only 0.792, and an average depth error of 1.822). The significant deviations in these scores confirm that current "high-quality" synthetic data often suffers from pervasive geometric inaccuracies (fig. 1(a)).

Addressing these challenges, we present Reinforcement Learning with Geometric Feedback (RLGF), a novel framework that injects perception-model-driven geometric spatial constraints directly into the video generation process. Unlike conventional approaches [57, 56, 61, 18] that primarily rely on pixelwise alignment, which often fail to explicitly enforce adherence to complex, underlying geometric principles, RLGF leverages dedicated, pre-trained AD perception models as reward providers to ensure geometric fidelity.

RLGF introduces two core technical innovations: Latent-Space Windowing Optimization and Hierarchical Geometric Alignment. Frist, we present Latent-Space Windowed Optimization. We observe that geometric structures in diffusion models emerge progressively across denoising steps: early steps

(e.g., before step 10 in flow matching [27, 28]) establish coarse global geometry, while later steps refine local details (fig. 1(c)). Training across the entire sampling process, as done in some prior RL-diffusion work [1] struggles to provide targeted guidance for these distinct phases. Therefore, we propose an efficient latent-space training strategy where rewards are applied directly to noisy latent features within a randomly sampled sliding window of intermediate diffusion steps. This approach significantly reduces computational (GPU memory) burden and, more importantly, allows for effective and targeted corrective feedback during both early (global structure formation) and late (detail refinement) stages of geometric synthesis.

Secondly, RLGF features Hierarchical Geometric Reward (HGR), a multi-level feedback system designed to imbue generated videos with robust geometric fidelity and scene coherence. HGA integrates signals from two specialized latent-space perception networks: a Latent Geometry Perception Model assessing vanishing point, lane, and depth cues, and a Latent Occupancy Prediction Model inferring 3D scene occupancy. Both operate efficiently on noisy latents, aided by a lightweight micro-decode module to circumvent costly full decoding. Leveraging these perception models, HGA then constructs its multi-level reward system. For point-line-plane geometric feedback, we use the outputs of the perception model to: (1) enforce vanishing point consistency for accurate global perspective, (2) ensure lane topology validity for realistic road structure, and (3) promote depth coherence for correct surface and object geometry. For scene-level occupancy feedback, outputs from the occupancy model are used to: (4) align intermediate semantic features using KL divergence for plausible scene evolution, and (5) maximize 3D occupancy IoU for accurate volumetric object layout and dynamics. This structured approach, combining efficient latent-space perception with a multi-faceted reward design, ensures that comprehensive geometric and occupancy information is extracted and effectively fed back to guide the video diffusion model towards producing physically principled and geometrically sound autonomous driving scenarios.

Our contributions are fourfold:

We are the first to systematically quantify the geometric distortion problem in autonomous driving video generation and propose the GeoScores metric for its evaluation.

We introduce RLGF, a novel paradigm that uses reinforcement learning with perceptionbased rewards applied efficiently within a sliding window in latent space, enabling plug-andplay geometric correction.

We design HGR which addresses geometric distortions by incorporating point-line-plane and scene-level occupancy multi-level geometric feedback derived from latent representations.

Extensive experiments on nuScenes demonstrate RLGF's plug-and-play effectiveness across two baselines [18, 9], boosting 3D detection mAP by 12.7% absolute while reduce geometry gap (via GeoScores) relative to real data. This work establishes a new paradigm for geometrically faithful synthetic data generation in autonomous driving systems.

2 Related Work

2.1 Video Diffusion for Autonomous Driving

The development of robust autonomous driving (AD) systems [15, 17, 6] necessitates large volumes of diverse and realistic training data. Learned generative model [22, 62, 11] have emerged as a powerful approach to synthesize such data by capturing the complex distributions of real-world driving scenarios. Early efforts explored Generative Adversarial Networks (GANs)[11, 38] and Variational Autoencoders (VAEs) [21] for generating driving-related imagery. More recently, diffusion models [12, 35, 44, 39, 59, 69, 68, 8], including latent diffusion models [44], have demonstrated state-of-the-art capabilities in generating high-fidelity images and videos [44, 4, 3, 77, 51, 54, 55, 75, 34]. Based on this technique, generative models in autonomous driving have greatly advanced. For example, BEVGen [46] and BEVControl [64] generate controllable street-view imagery from Bird's-Eye View (BEV) layouts or other structural conditions. Further advancements, such as DriveDreamer [53], Magicdrive [10],DriveWM [56], Panacea [57], DriveSphere [61] and others [76, 30, 31, 58, 18, 23, 32, 73, 72], focus on generating coherent multi-camera driving videos, often conditioned on textual prompts, historical trajectories, or 3D assets. These models excel at producing visually compelling outputs that achieve high scores on perceptual metrics like Fréchet Video Distance (FVD) [49].

Despite achieving high visual realism, leading AD video generation models [57, 9, 18] frequently introduce subtle geometric and scene-level distortions (e.g., flawed perspectives, depth errors, unrealistic motion). These inconsistencies, often overlooked by human assessment, severely undermine performance on 3D perception tasks like object detection [26] and motion forecasting. RLGF addresses this critical gap by proposing a novel framework to instill multi-scale geometric and scene-level consistency within the generation process.

2.2 Reinforcement Learning for Video Generation

Fine-tuning generative models with reward signals, rooted in Reinforcement Learning from Human Feedback (RLHF) successes in LLMs (e.g., using PPO [45] or DPO [41]), is increasingly applied to diffusion models. For image generation, methods [2, 50, 63, 60] align models with human preferences, and approaches [25] use perception model feedback for content control. This trend extends to video diffusion, where DPO-based techniques [66, 74, 71] often utilize human preference data. While improving general video quality, these holistic preference scores typically lack the precise, local geometric feedback crucial for autonomous driving applications. Other methods [1, 24, 33, 7] employ explicit reward models for video fine-tuning, offering more detailed signals. However, these rewards are generally not tailored to the specific multi-scale 3D geometric and physical plausibility demands of AD simulation, failing to adequately address distortions like incorrect perspective or depth error.

Our RLGF framework advances this by introducing explicit, quantifiable geometric and scene-level rewards from dedicated AD perception models. This provides targeted, locally-aware feedback to correct specific geometric inaccuracies, producing data suitable for rigorous AD tasks.

3 Method

3.1 Preliminary: Limitations of Conditional Video Diffusion

Current video diffusion models [3, 57, 18] generate frames by gradually denoising latent variables through a Markov chain. Formally, given a condition c (e.g., road sketches or bounding boxes), the model learns to minimize the pixel-level reconstruction error during training:

where x t is the noisy sample at timestep t , ϵ is the ground truth noise, and ϵ θ is the denoising network. While effective for visual fidelity, this standard formulation inherently ignores crucial geometric-semantic correlations due to two limitations: (1) Pixel-wise Independence Assumption: The MSE loss treats each pixel as independent, failing to model higher-order geometric relationships (e.g., perspective consistency in 3D space). (2) Conditional Oversimplification: Existing methods concatenate geometric conditions c with noisy latents, However, this primarily enforces local pixel alignment corresponding to the condition rather than guaranteeing the global geometric integrity or plausibility of the underlying 3D scene structure. These limitations motivate the need for explicit geometric guidance during generation.

3.2 Reinforced Learning with Geometric Feedback (RLGF)

Our primary objective is to enhance the geometric and spatio-temporal consistency of videos generated by conditional diffusion models, addressing the critical gap left by conventional training objectives that primarily focus on pixel-level visual fidelity.

To achieve this, we introduce Reinforcement Learning with Geometric Feedback (RLGF). This framework refines a pre-trained video diffusion model, ϵ θ , by guiding its generation process towards outputs that exhibit greater adherence to real-world geometric and physical principles. Unlike RLHF [37, 63], which typically captures broad subjective preferences, RLGF is designed to incorporate specific, model-interpretable geometric constraints. The core idea is to leverage dedicated, pre-trained perception models to evaluate the generated videos x 0 and provide a reward signal that steers ϵ θ away from implausible outcomes.

Formally, given a well-trained video diffusion model ϵ θ , the dataset D v , the well-designed reward function R ( · ) , we aim to maximize the objective:

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where c and v denote the condition and real video from the dataset D v , x 0 the the generated sample by ϵ θ ( ·| c ) given c , and the total reward R ( x 0 , v ) encapsulates assessments from our Hierarchical Geometric Alignment system, as detailed in Section 3.4.

3.3 Latent-Space Windowing RL Optimization

Directly optimizing Equation 2 by unrolling the entire T -step diffusion sampling process to obtain x 0 and then backpropagating the reward gradient ∇ θ R ( x 0 , v ) is computationally prohibitive due to large memory requirements and long sampling chains.

To efficiently apply RLGF, we propose Latent-Space Windowed RL Optimization. This strategy addresses the computational challenge of full rollouts and leverages insights into the progressive nature of structure formation in diffusion models.

Visualizations (e.g., fig. 2(c) in Introduction) suggest that coarse global geometry is often established in earlier denoising steps (e.g. t > T mid ), while later steps refine details. Training across the entire sampling chain, as in some prior RL-diffusion work [1], may not provide sufficiently targeted guidance for these distinct phases.

Instead of rewarding only the final output x 0 , we provide feedback based on noisy latent features z t at intermediate denoising steps t , compared against reference latents z v derived from real videos. Specifically, during the T -step sampling process z T -→ ··· → z 0 , we apply our reward functions R within a sliding window of w steps (e.g. from random start step t ′ down to k = t ′ -w ). Our perception models are designed to take z k and k as input.

This approach is motivated by two factors: (1) Efficiency: It significantly reduces the computational graph for backpropagation. (2) Effectiveness: It allows targeted corrective signals at different stages of generation, crucial for both global structure and local detail.

The practical objective then becomes maximizing the expected reward obtained by evaluating the noisy latent z k (at step k within the window) against the VAE-encoded latent z v paired with real

video v :

where and z v is the encoded latent feature using V AE ecnoder [44]. The gradient with respect to the LoRA parameters θ LoRA for a reward at step k is then:

Throughout the RLGF fine-tuning, the perception models that constitute the reward function are pre-trained and their weights remain frozen. ensuring a consistent evaluation standard as ϵ θ via θ LoRA adapts to maximize geometric fidelity.

3.4 Hierarchical Geometric Reward (HGR)

The Hierarchical Geometric Reward ( R ) is designed to provide comprehensive, multi-level feedback on the geometric integrity and scene coherence of generated video latents. It evaluates consistency across point, line, plane, perceptual feature, and voxel-level representations, derived by applying specialized perception models to both the generated latent z k and the reference real-video latent z v .

3.4.1 Efficient Latent-Space Perception

To avoid computationally expensive full VAE decoding at each step k for perception, our perception models operate directly on latent features. Given a noisy video latent z k ∈ R L × C × H ′ × W ′ (where L is frames, C channels, H ′ , W ′ latent dimensions) at diffusion step k , we first employ a lightweight Micro-Decode Module, F micro . This module, constructed using shallow layers from the VAE decoder, processes z k and k to produce enhanced per-frame features:

where k is processed by the Fourier Embedding [77, 48]. f f k is suitable for downstream perception tasks, significantly reducing memory and computation. The same F micro is applied to z v (with k = 0 ) to obtain reference features f f v .

3.4.2 Perception Model Architectures

We train two models that take these micro-decoded features { f f k } or { f f v } and timestep k as input.

Latent Geometry Perception Model ( P geo ): This multi-task model processes per-frame features f f to assess static 2.5D geometry. It uses a DINOv2 [36] backbone followed by task-specific heads:

(1) For vanishing point detection , we reformulate the task as heatmap regression to better capture positional uncertainty, following [13]. The network head takes g as input and predicts a probability heatmap H ∈ R h × w . The ground truth heatmap H gt is constructed as a 2D Gaussian centered at the annotated VP location, balancing localization precision and learning difficulty. We optimize using MSE:

(2) The lane parsing branch complements this by detecting road markings through a topology-aware segmentation head, following [42].

(3) As for the depth estimation task, we fine-tune the weight of [65] using a scale-invariant logarithmic (SiLog) loss:

where d i = log y i -log y gt i , y i is the predicted results and λ ∈ [0 , 1]

Finally, we optimize P geo using the total loss L geo = L vp + L lane + L depth .

Latent Occupancy Prediction Model ( P occ ): This model processes per-frame features f f using an image backbone followed by a prediction head to infer the intermediate backbone features feat occ and the 3D occupancy grid O i ∈ R X × Y × Z representing the occupied space around the ego vehicle for that frame. P occ is trained based on [16].

3.4.3 Multi-Granularity Reward Signals

The hierarchical geometric reward R = R geo + R occ quantifies geometric fidelity and scene coherence through a multi-scale decomposition.

For R geo , it addresses vanishing point consistency, lane topology validity, and depth coherence. Given the outputs { p vp , L pred , D pred } from P geo ( z k ) and reference conditions { v ref , L ref , D ref } from P geo ( z v ) , we define:

where p vp , p vp , p vp are the predictions from P geo for vanashing point, lanes and depth, respectively. λ vp , λ vp and λ vp weights balancing the contribution of each task. The individual reward functions r ( · ) are designed to be high for geometrically accurate samples and low (or negative) for inconsistent ones:

where v ref is the ground-truth vanishing point in conditions.

where F1-Score indicates the F1-Scores.

where pixel masks M road and M vehicle are generated from real data and identify road and vehicle regions, respectively.

After that, we define R occ ( z k , z v ) = r align + r iou . r align encourages similarity in high-level scene interpretation by aligning distributions of intermediate occupancy features. For each frame f :

where feat occ,f are backbone features from P occ . Distributions p ( · ) can be estimated (e.g., Gaussian fit over a batch).

r occ promotes accurate 3D structure and object layout. For each frame f :

where O f are the 3D occupancy grids from P occ .

4 Experiments

4.1 Experimental Setup

We conduct comprehensive experiments to validate the efficacy of our proposed Reinforcement Learning with Geometric Feedback (RLGF) framework. This section details the datasets, baseline models, evaluation metrics, and implementation specifics used in our evaluation.

Datasets. Our experiments are primarily conducted on nuScenes [5] using the official validation split. Its multi-camera setup and comprehensive annotations, including 3D object labels and HD maps, provide a challenging benchmark. For this dataset, conditions c for the diffusion models are extracted from ground truth annotations, simulating realistic inputs for controllable generation.

Baselines. Wedemonstrate the plug-and-play nature of RLGF by integrating it with two representative video diffusion models, referred to as MagicDrive-V2 [9] and DiVE [18].

Evaluation Metrics. GeoScores: We use our proposed GeoScores suite: Vanishing Point Error: NormDist between the predicted VP and the pseudo-ground truth VP derived from real data. Lower is better. Lane Topology Score: F1-score for semantic segmentation of lane markings against ground

truth lane masks. Higher is better. Depth Error: We use RMSE between predicted depth for road surface regions and pseudo-ground truth depth. Lower is better.

Downstream Task Performance: 3D Object Detection: We employ a strong BEV-based detector, BEVFusion [26], trained solely on synthetic data generated by different methods or on real data. We report the standard nuScenes detection score (NDS) and mean Average Precision (mAP).

Visual Realism: We report Fréchet Video Distance (FVD) [49] to ensure RLGF does not degrade the visual quality achieved by the baseline diffusion models.

Implementation Details are included in the supplemental materials.

4.2 Performance of Pre-trained Perception Models

We first verify the capabilities of our perception models ( P geo and P occ which form the basis of our reward functions. These models operate directly on latent features z t and timestep t . Our goal here is to demonstrate their competence in extracting meaningful geometric and scene information from the latent domain.

Latent Geometry Perception Model ( P geo ) is a multi-task model responsible for assessing finegrained geometric properties from latent features. Table 1 presents its performance on the nuScenes validation split for vanishing point (VP) detection, lane parsing, and depth estimation, compared against established pixel-space methods. The results indicate that P geo effectively captures these geometric cues from latent features. For instance, it achieves a normalized distance error of 0.024 for VP detection and an F1-score of 0.865 for lane parsing. Although its depth estimation RMSE (2.596) is higher than the specialized pixel space model DepthAnything-v2, it still provides a consistent measure of depth relationships.

Latent Occupancy Model ( P occ ) is tasked with understanding 3D scene layout from sequences of latent features. Its performance on the Occ3D-nuScenes is shown in Table 3. P occ achieves an overall mIoU of 29.96 when operating from latent representations. This level of performance, compared to a pixel-based method like FlashOcc (32.08 mIoU), demonstrates a solid capability to infer volumetric scene structure from the latent domain.

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4.3 Main Results: Improving Geometric Fidelity and Downstream Tasks

Table 2 shows RLGF's impact on the nuScenes validation set. RLGF consistently enhances geometric integrity (GeoScores) across baselines while maintaining or improving visual quality (FVD). For instance, DiVE + RLGF significantly improves all GeoScore components (VP error: 0.086 → 0.068; Lane F1: 0.792 → 0.879; Depth RMSE: 1.822 → 0.772).

Crucially, this geometric enhancement translates to substantial 3DOD gains: DiVE + RLGF boosts mAP from 25.75 to 31.42 and NDS from 33.61 to 36.07, markedly closing the gap to real data performance. Similar improvements are seen for MagicDrive-v2 + RLGF (mAP: 18.95 → 23.21). These results underscore RLGF's effectiveness as a plug-and-play module for improving both geometric soundness and downstream utility of synthetic videos. We also present the qualitative result in fig. 3 about the detection results on our synthetic data and baseline [18] and the real data. (More qualitative visualization results and further analysis are included in the supplementary materials).

4.4 2D Object Detection Results

To illustrate that current diffusion models like DiVE can generate visually realistic data with minimal 2D domain gap for certain tasks, we present qualitative 2D object detection results. Figure 4 shows outputs from a YOLOv5 [20] detector applied to (a) real nuScenes data and (b) synthetic data generated by the DiVE baseline. The detector is pre-trained on a large-scale dataset (e.g., COCO) and then fine-tuned on real nuScenes training data.

As observed in Figure 4, the 2D detection performance on DiVE-generated synthetic data is qualitatively very similar to that on real data. Objects are generally detected with comparable confidence and bounding box accuracy. This visual consistency aligns with our quantitative findings (mAP: 43.8 on synthetic vs. 44.7 on real, as mentioned in the Introduction), suggesting that the semantic content and 2D appearance features necessary for 2D detection are well-preserved in the synthetic videos. This further reinforces our hypothesis that the primary limitation of such synthetic data lies in its 3D geometric fidelity, which is specifically addressed by our RLGF framework.

4.5 Ablation Study

4.5.1 Ablations about HGA Reward Components

We conduct ablation studies on nuScenes using DiVE as the baseline to understand the contributions of different HGA reward components. Results are shown in Table 4. Incrementally adding reward components ( r vp , r lane , r depth , and occupancy rewards ( r align + r iou ) progressively improves 3DOD performance (Table 4). For example, adding point-line-plane rewards (ID: 3) boosts DiVE's mAP to 27.12. Incorporating only occupancy rewards (ID: 4) yields 28.06 mAP. The full HGA system, combining all five components, achieves the highest mAP (31.42) and NDS (36.07), highlighting the synergistic benefits of our comprehensive multi-level feedback. (More qualitative visualization results and further analysis are included in the supplementary materials).

4.5.2 Ablations about Hyerparameters.

We provide the detailed ablation results in table 5 for clarity, including the RL window size, reward weights, and the starting step of the sliding window. All ablations are performed on the DiVE baseline and evaluated with BEVFusion.

5 Conclusion

In this work, we addressed the critical issue of geometric distortions in diffusion-based video generation for autonomous driving. We introduced GeoScores for quantitative evaluation and proposed Reinforcement Learning with Geometric Feedback (RLGF), a novel framework to enhance the geometric integrity of synthetic videos. RLGF, through its Hierarchical Geometric Alignment (HGA) module which incorporates multi-level geometric and scene occupancy feedback, effectively injects perception-driven constraints into the generation process by fine-tuning pre-trained diffusion models. Our experiments demonstrate that RLGF significantly improves geometric fidelity across multiple baselines and, crucially, boosts downstream 3D object detection performance by up to , substantially closing the gap with real-data performance. This work establishes a new direction for generating more reliable and task-aware synthetic data for autonomous systems.

Acknowledgments

This work was supported by the Science and Technology Development Fund of Macau SAR (FDCT) under grants 0102/2023/RIA2 and 0154/2022/A3 and 001/2024/SKL, and the Jiangyin Hi-tech Industrial Development Zone under the Taihu Innovation Scheme (EF2025-00003-SKL-IOTSC).

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Appendix

This supplementary material provides additional details to support the findings presented in our main paper. We include: (1) comprehensive implementation specifics for our RLGF framework and the pre-trained perception models; further details on the GeoScores metric computation; (2) additional experimental results (3) a discussion on the limitations of our current work and potential future directions.

A Detailed Implementation Details

This section elaborates on the implementation details of our proposed RLGF framework, the pretrained perception models ( P geo and P occ ) , and the experimental setup.

A.1 Dataset Preparation

All experiments, including the pre-training of our perception models ( P geo and P occ ) and the finetuning of diffusion models with RLGF, are conducted using the nuScenes dataset [5]. We primarily utilize the official training and validation splits. While nuScenes provides rich annotations like 3D bounding boxes and HD maps, it does not directly offer ground truth labels for vanishing points (VP), dense segmentation masks for all relevant classes (like fine-grained lanes beyond HD map polylines), or per-pixel depth maps required by our P geo . Therefore, we generate high-quality pseudo-labels for these tasks using strong, pre-existing perception models, as detailed below. These pseudo-labels serve as the training targets for our latent-space perception models.

Depth Pseudo-Labels: To obtain dense depth information for training the depth estimation component of P geo , we utilize Depth Anything V2 (vit-l version) [65]. This state-of-the-art monocular depth estimation model is applied to all images in the nuScenes training set to generate per-pixel depth maps. These output depth maps serve as the pseudo-ground truth for our latent depth estimation task.

Semantic Segmentation Pseudo-Labels (Lanes, Road Surface, Vehicles): For precise segmentation masks of various scene elements, we employ Grounded-SAM-2[43, 42]. For the lanes, the model is prompted to accurately segment visible lane markings. The resulting binary segmentation masks are used as pseudo-ground truth for training the lane parsing head of P geo . For the road surface and vehicle masks, SAM-2 is also utilized to generate segmentation masks for road surfaces and vehicles.

Vanishing Point Pseudo-Labels from Lane Masks, following [13]: With accurate lane segmentation masks obtained via SAM-2 (as described above), we derive vanishing point pseudo-labels through a geometric procedure. For each detected lane marking in a frame: The center point of the lane marking is calculated from its left and right edges (derived from the SAM-2 segmentation mask) for every horizontal image line at 5-pixel intervals. These extracted center points are grouped to represent the centerline of each individual lane marking. Robust curve fitting (e.g., RANSAC with a line model) is applied to these centerlines. The intersection point of multiple fitted lane centerlines is then computed to determine the scene's vanishing point. This computed VP serves as the pseudo-ground truth for the VP detection task.

The use of these high-quality pseudo-labels enables us to train effective latent-space perception models tailored to the nuScenes domain, which subsequently provide the nuanced reward signals for our RLGF framework. The conditions c for the main diffusion models (e.g., semantic 3D boxes for some baselines) are derived from the original nuScenes ground truth annotations.

A.2 Perception Model Architectures and Pre-training

Micro-Decode Module( F micro ) : The F micro module is constructed using the first upper block of the official V AE decoder from the OpenSora [77] used by our baseline video diffusion models. The input of F micro is the noisy latent feature z f k for a frame f with the timestep k . The same F micro architecture is used when processing the reference real-video latent z v (with k typically set to 0).

Latent Geometry Perception Model( P geo ) : We use a pre-trained DINOv2-ViT-S/14 [36] as the backbone feature extractor and the pre-trained weight from DepthAnything-V2 [65]. P geo is trained

for 50 epochs on the nuScenes training split using the AdamW optimizer with a learning rate of 5 × 10 -5 and a batchsize of 16. We use 8 × NVIDIA A100 GPUs to cover the experiment.

Latent Occupancy Prediction Model( P occ ) : P occ is trained on occ3D-nuscenes dataset for 24 epochs using AdamW optimizer following [70, 16].

A.3 RLGF Fine-tuning Details

Baselines: We used publicly available checkpoints for MagicDrive-V2 [9] and DiVE [18].

LoRA Configuration: For LoRA, we applied it to the attention layers (Q, K, V projections) of the DiT backbone in the diffusion models. We used a rank r = 16 following [1].

Latent-Space Windowed Optimization: The window size w is set to 5. The starting step t ′ for the window was randomly sampled from the range [8 , 30] , with T = 30 is the total number of diffusion steps.

Reward Weights: We set λ vp = 0 . 1 , λ lane = 0 . 1 , λ depth = 0 . 5 . These weights were determined empirically based on early experiments on a small validation subset, aiming to balance the scale of individual reward components and their perceived impact on generation quality.

We use AdamW with a learning rate of 1 × 10 -4 and a batchsize of 1 with 8 frames per video clip.

A.4 GeoScores Metric Details

This section provides further clarification on the computation of our GeoScores components. For all GeoScores, the "reference ground truth" is derived by applying the corresponding pre-trained perception model to the real video data, following appendix A.1. The score then measures the deviation of the synthetic video's perception output from this real-data-derived reference.

Vanishing Point Error (VP ↓ ): Calculated as the L2 Normalized Distance (NormDist) between the calculated VP on a synthetic frame and the VP calculated on a real frame. Lane Topology Score (Lane ↑ ): Calculated as the F1 score for semantic segmentation of lane markings. The predictions is from Grounded-SAM2 [43, 42] on the synthetic frame, and the target is applied to the real frame. Depth Error (Depth) ↓ : Calculated as the Root Mean Squared Error (RMSE) between the depth map predicted by Depth Anything V2 [65] for road surface regions on a synthetic frame and the depth map for the same regions on the real frame. Road surface masks are obtained from SAM-2 [42].

B Additional Experiment Results

B.1 Extended 3D Object Detection Results on Multiple Detecters

To further demonstrate the generalizability of the improvements conferred by RLGF, we evaluated the generated synthetic data using an additional state-of-the-art 3D object detector, StreamPETR [52], alongside the BEVFusion results presented in the main paper. Table 6 presents the performance (mAP and NDS on nuScenes validation) for StreamPETR and the average performance across both BEVFusion and StreamPETR. Both detectors were trained from scratch solely on the respective synthetic data or real data.

B.2 Robustness to Different Downstream Detectors:

To demonstrate generality, we evaluated our method on the stronger StreamPETR detector. RLGF achieved a substantial +4.75% mAP gain, proving our geometric improvements are robust and benefit diverse downstream architectures.

C Limitations and Future Work

This section discusses the current limitations of our RLGF framework and GeoScores metric, alongside potential avenues for future research.

Dependence on Perception Models: RLGF's performance is inherently tied to the accuracy and robustness of the pre-trained perception models ( P geo , P occ ). Biases or errors in these models could

propagate into the reward signal and mislead the generation process. Future work could explore jointly training or adapting perception models during RLGF, or using ensembles.

Computational Cost: While Latent-Space Windowed Optimization significantly reduces costs compared to full rollouts, RL-based fine-tuning remains more computationally intensive than standard diffusion model training. Exploring more sample-efficient RL algorithms or distillation techniques could be beneficial.

Reward Design and Balancing: The current HGA reward combines five components with manually tuned weights. Optimizing these weights automatically or learning a more adaptive reward function is a promising direction. Furthermore, incorporating even more diverse geometric or physical constraints (e.g., collision avoidance, traffic rule adherence) could further enhance realism.

Generalization: While demonstrated on nuScenes, further investigation is needed to assess RLGF's generalization capabilities across diverse datasets, environmental conditions (e.g., adverse weather, night scenes not well-represented in training), and different diffusion model architectures.

GeoScores Scope: Current GeoScores focus on camera-based geometric aspects. Expanding them to include LiDAR consistency or multi-modal geometric agreement could provide a more holistic evaluation.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors presents RLGF, a method for fine-tuning diffusion-based driving video generators using reinforcement learning with geometric feedback. The method employs a lightweight LoRA modules and optimizing them with a PPO-style RL algorithm. RLGF leverages a hierarchical reward that captures vanishing point alignment, lane structure, depth accuracy, and 3D occupancy. The method significantly reduces geometric distortions of the generated videos, and improves downstream 3D object detection performance on nuScenes trained with synthetic data.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n1. The paper addresses an important issue in driving video generation, geometric distortion, which can significantly impact downstream 3D perception. The authors provide an effective solution by leveraging RL with geometric feedback and demonstrate solid improvements in both geometric consistency and task performance.\n2. The design of the Hierarchical Geometric Reward (HGR), which incorporates perception models in latent space and uses multi-level geometric cues, is novel and insightful. It could inspire further research in both generative modeling and autonomous driving. \n3. Table 1 and Table 4 provide interesting insights into the effectiveness of latent-space perception and the contribution of each reward component, offering valuable results.\n\nWeaknesses\n1. All experiments are conducted using only synthetic data. Neither the main paper nor the supplementary material includes results that combine real and synthetic data during training, which is essential for evaluating practical effectiveness. For comparison, Table 2 of the DiVE paper includes such experiments. \n2. The evaluation is also limited to 3D object detection. Including more downstream tasks, such as BEV segmentation, object tracking, etc, would make the paper more comprehensive. \n3. The choice to use BEVFusion as the main detector in Table 1 is questionable, as it underperforms compared to stronger models like StreamPETR, which are commonly used in related work. This results in lower baseline results compared with prior works. Although I see a table of using StreamPETR in the supplementary material, the performance of some baseline methods is not complete. \n4. The RL pipeline seems heavily dependent on a capable latent perception model. The performance of the latent perception model might be hinder larger performance growth on larger models / dataset.\n\nQUESTIONS:\n1. How would the model perform under a synthetic+real dataset setting, similar to Table 2 of the DiVE paper and Table 3 of the GLAD paper (Glad: A Streaming Scene Generator for Autonomous Driving)? This experiment is crucial in evaluating the practical effectiveness of the proposed method. \n2. A more comprehensive experiment is needed on the StreamPETR detectors instead of BEVFusion, aligning with prior works. \n3. How would the model perform if the video diffusion model is fine-tuned with RL and less capable perception models? It seems that the RL pipeline is heavily dependent on a capable latent perception model. Would the latent perception model hinder the upper bound of the usefulness of the synthetic data generator?\n\nLIMITATIONS:\nThe authors discussed the limitations in the supplementary materials.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper proposes reinforcement learning with geometric feedback, to make the generative model learn to capture 3D scene structure rather than only 2D appearance.  The main innovation lies in Latent-Space Windowing RL Optimization to be both effective and efficient, and Hierarchical Geometric Reward to provide multi-level feedback. The results show that the geometric consistency of generated images is greatly improved.

STRENGTHS AND WEAKNESSES:
Strengths:
* This work identities the problem of geometric distortions in current generative models, which is important for driving task.
* Latent-Space Windowing Optimization is introduced to enable learning on noisy latent, without the need for complete sampling chain.
* Multi-Granularity Reward Signals are proposed to enhance the geometric consistency of generative model on multi-levels.
* Evaluation results on detection task show great improvement compared to baseline without RLGF.

Weaknesses:
* There are some typos in paper writing,  e.g. Line 162, fig.2(c), Line 304, up to.
* The generative model is video diffusion model, while the four geometric perception tasks take image as input, which seems to have the potential for further improvement.

QUESTIONS:
* What is reason to choose these perception tasks,  without using detection as a perception task, which aligns better with evaluation?
* As I am not an expert of RL, could you explain the relationship between your model learning with RL?

LIMITATIONS:
No major limitations, see Weaknesses.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis manuscript aims to address geometric inaccuracies prevalent in synthetic videos generated for AD. The authors present RLGF (Reinforcement Learning with Geometric Feedback), a reinforcement learning framework tailored to enhance video diffusion models with geometrically coherent outputs. \n\nRLGF introduces two components: \n- Latent-Space Windowing Optimization, providing efficient and targeted reinforcement during intermediate diffusion stages.\n- Hierarchical Geometric Reward (HGR), leveraging latent-space perception models to provide detailed geometric constraints (vanishing point consistency, lane structure coherence, and accurate depth). \n\nThe manuscript further introduces \"GeoScores”, a suite of metrics designed explicitly to quantify geometric fidelity in synthetic videos. \n\nEvaluations on the nuScenes dataset demonstrate good geometric improvements and substantial performance gains in downstream tasks, notably 3D object detection.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n(+) The manuscript identifies and clearly motivates an important yet overlooked limitation in existing AD synthetic data—geometric distortion affecting downstream perception tasks. The introduction of latent-space windowed RL optimization and hierarchical geometric rewards advances video diffusion training methods. \n\n(+) The GeoScores metric suite provides a way of quantifying geometric inaccuracies. This strengthens the manuscript by defining the problem and providing a reproducible method for evaluation. Experiments demonstrate the efficacy of RLGF. Particularly is the considerable reduction in geometric errors and good improvements in 3D object detection.\n\n(+) The manuscript is overall coherent, structured, and with supporting visualizations and tables that enhance readability and understanding.\n\n---\n\n### Weaknesses\n\n(-) RLGF introduces significant complexity via reinforcement learning in the latent space and multiple perception-based rewards. A deeper exploration into computational demands (runtime, memory usage) and scalability is needed for practical considerations.\n\n(-) The effectiveness of RLGF heavily relies on pre-trained perception models. A thorough analysis or discussion of how inaccuracies or limitations within these perception models may affect the geometric rewards would strengthen robustness claims.\n\n(-) Evaluations are conducted solely on the nuScenes dataset. The generalization of RLGF's performance to other autonomous driving datasets or sensor setups remains unclear and is not sufficiently discussed.\n\n(-) The latent-space windowing optimization seems to have a good motivation, yet critical parameters (e.g., window size and position) are not extensively ablated, potentially obscuring the sensitivity and robustness of the proposed method.\n\nQUESTIONS:\n1. Based on Weakness #1: Can you provide detailed computational complexity analyses or runtime benchmarks for the introduced latent-space windowing optimization and hierarchical rewards?\n\n---\n\n2. Based on Weakness #2: How robust is RLGF to inaccuracies or biases inherent within the perception models used for generating geometric rewards?\n\n---\n\n3. Based on Weakness #3: Have you tested or considered the generalization capability of your method across datasets beyond nuScenes, or across varying sensor configurations and environmental conditions?\n\n---\n\n4. Based on Weakness #4: Can you provide additional insights or experimental results regarding how the choice of window size and position impacts model performance?\n\n---\n\nAdditional questions (minor):\n\n5. It is suggested to provide additional qualitative visualizations to demonstrate intermediate latent states before and after RLGF optimization.\n\n---\n\n6. It is suggested to explicitly clarify hyperparameter choices for RL window size and reward weights.\n\nLIMITATIONS:\n- The model might require extensive tuning due to multiple interdependent reward components and RL optimization hyperparameters, possibly limiting straightforward applicability.\n\n- The computational complexity introduced by the latent-space optimization and multiple perception-based rewards may limit deployment to high-resource environments, challenging real-time or resource-constrained applications.\n\n- The geometric rewards depend on the performance and reliability of perception models, which might propagate errors or biases into the final synthetic data.\n\n- Without broader evaluations across diverse AD scenarios, the generalizability of this method to other datasets, sensors, or environmental conditions remains uncertain.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a novel framework, Reinforcement Learning with Geometric Feedback (RLGF), aimed at improving the geometric fidelity of synthetic video generation for autonomous driving. The authors identify a critical yet underexplored issue in current state-of-the-art video generation models: subtle geometric distortions that limit their utility for downstream perception tasks. RLGF addresses this by incorporating specialized perception models as reward providers to refine video diffusion models. The results demonstrate significant improvements in geometric accuracy and downstream task performance. This is a well-executed and innovative contribution to the field.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Propose the GeoScores metric for systematically evaluating and quantifing the geometric distortion problem in autonomous driving video generation.\n2. Offers a plug-and-play solution for generating geometrically sound and reliable synthetic videos for AD development.\n3. Allow for effective and targeted corrective feedback during both early (global structure formation) and late (detail refinement) stages of geometric synthesis.\n4. The article logic and writing expression are smooth.\n\nWeaknesses：\n1. The experiments are not sufficient. For example, there are no control experiments on the robustness of different GT detectors and the balance between different rewards.\n2. Why VP, Lane, and Depth losses can cover geometric constraints is not fully explained. For example, occlusion relationships and temporal continuity are not included in the above losses.\n3. There is a lack of discussion on the shortcomings of the method and the direction of further improvement.\n\nQUESTIONS:\n1. Why choose only these two frozen perception models Pgeo (point-line-plane alignment) and Pocc (scene-level consistency) instead of others?\n2. How to constrain the temporal consistency in this framework?\n3. How are the comparative experiments of different RL Windowsize? It is recommended to add corresponding experimental results.\n4. How robust is the method to different detectors as rewards? It is recommended to add corresponding experimental results.\n5. How to balance the selection of hyperparameters between different geometric constraint signals? It is recommended to add corresponding experimental results.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}