Text Driven Visual Synthesis With Latent Diffusion Prior Demo We introduce scenario dreamer, a fully data driven generative simulator for autonomous vehicle planning that generates both the initial traffic scene (comprising the lane graph and agent bounding boxes) and closed loop agent behaviours. Scenario dreamer: vectorized latent diffusion for generating driving simulation environments published in: 2025 ieee cvf conference on computer vision and pattern recognition (cvpr).
Text Driven Visual Synthesis With Latent Diffusion Prior Demo We introduce scenario dreamer, a fully data driven generative simulator for autonomous vehicle planning that generates both the initial traffic scene comprising a lane graph and agent bounding boxes and closed loop agent behaviours. Below, we show examples of each of the supported generation modes with the scenario dreamer vectorized latent diffusion model: initial scene generation, lane conditioned object generation, and scene inpainting. While the scenario dreamer paper reports results at 165k steps, training to 250k steps leads to improvements across most metrics. for this reason, we are releasing the 250k step checkpoints and the expected results are marginally better than those reported in the paper. We introduce scenario dreamer, a fully data driven gen erative simulator for autonomous vehicle planning that generates both the initial traffic scene—comprising a lane graph and agent bounding boxes—and closed loop agent behaviours.
Cvpr Poster Neural Lens Modeling While the scenario dreamer paper reports results at 165k steps, training to 250k steps leads to improvements across most metrics. for this reason, we are releasing the 250k step checkpoints and the expected results are marginally better than those reported in the paper. We introduce scenario dreamer, a fully data driven gen erative simulator for autonomous vehicle planning that generates both the initial traffic scene—comprising a lane graph and agent bounding boxes—and closed loop agent behaviours. Scenario dreamer instead employs a novel vectorized latent diffusion model for initial scene generation that directly operates on the vectorized scene elements and an autoregressive transformer for data driven agent behaviour simulation. In contrast, scenario dreamer l generates significantly more realistic lane graphs, demonstrating the effectiveness of the proposed vectorized approach. figure 2 illustrates the inpainting capabilities of the scenario dreamer l model trained on the waymo dataset. Figure 2. vectorized environments generated by scenario dreamer with the proposed vectorized latent diffusion model trained on the waymo dataset (top row) and nuplan dataset (bottom row). [cvpr 2025] official repository for scenario dreamer: vectorized latent diffusion for generating driving simulation environments.
Cvpr Poster Image Neural Field Diffusion Models Scenario dreamer instead employs a novel vectorized latent diffusion model for initial scene generation that directly operates on the vectorized scene elements and an autoregressive transformer for data driven agent behaviour simulation. In contrast, scenario dreamer l generates significantly more realistic lane graphs, demonstrating the effectiveness of the proposed vectorized approach. figure 2 illustrates the inpainting capabilities of the scenario dreamer l model trained on the waymo dataset. Figure 2. vectorized environments generated by scenario dreamer with the proposed vectorized latent diffusion model trained on the waymo dataset (top row) and nuplan dataset (bottom row). [cvpr 2025] official repository for scenario dreamer: vectorized latent diffusion for generating driving simulation environments.
Cvpr Poster Slimmable Dataset Condensation Figure 2. vectorized environments generated by scenario dreamer with the proposed vectorized latent diffusion model trained on the waymo dataset (top row) and nuplan dataset (bottom row). [cvpr 2025] official repository for scenario dreamer: vectorized latent diffusion for generating driving simulation environments.
Cvpr Poster Learning Customized Visual Models With Retrieval Augmented
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