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Hangjun Ye

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2 papers

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NeurIPS Conference 2025 Conference Paper

Genesis: Multimodal Driving Scene Generation with Spatio-Temporal and Cross-Modal Consistency

Xiangyu Guo
Zhanqian Wu
Kaixin Xiong
Ziyang Xu
Lijun Zhou
Gangwei Xu
Shaoqing Xu
Haiyang Sun

We present Genesis, a unified world model for joint generation of multi-view driving videos and LiDAR sequences with spatio-temporal and cross-modal consistency. Genesis employs a two-stage architecture that integrates a DiT-based video diffusion model with 3D-VAE encoding, and a BEV-represented LiDAR generator with NeRF-based rendering and adaptive sampling. Both modalities are directly coupled through a shared condition input, enabling coherent evolution across visual and geometric domains. To guide the generation with structured semantics, we introduce DataCrafter, a captioning module built on vision-language models that provides scene-level and instance-level captions. Extensive experiments on the nuScenes benchmark demonstrate that Genesis achieves state-of-the-art performance across video and LiDAR metrics (FVD 16. 95, FID 4. 24, Chamfer 0. 611), and benefits downstream tasks including segmentation and 3D detection, validating the semantic fidelity and practical utility of the synthetic data.

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NeurIPS Conference 2025 Conference Paper

Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers

Gangwei Xu
Haotong Lin
Hongcheng Luo
Xianqi Wang
JINGFENG YAO
Lianghui Zhu
Yuechuan Pu
Cheng Chi_

This paper presents Pixel-Perfect Depth, a monocular depth estimation model based on pixel-space diffusion generation that produces high-quality, flying-pixel-free point clouds from estimated depth maps. Current generative depth estimation models fine-tune Stable Diffusion and achieve impressive performance. However, they require a VAE to compress depth maps into the latent space, which inevitably introduces flying pixels at edges and details. Our model addresses this challenge by directly performing diffusion generation in the pixel space, avoiding VAE-induced artifacts. To overcome the high complexity associated with pixel-space generation, we introduce two novel designs: 1) Semantics-Prompted Diffusion Transformers ( SP-DiT ), which incorporate semantic representations from vision foundation models into DiT to prompt the diffusion process, thereby preserving global semantic consistency while enhancing fine-grained visual details; and 2) Cascade DiT Design that progressively increases the number of tokens to further enhance efficiency and accuracy. Our model achieves the best performance among all published generative models across five benchmarks, and significantly outperforms all other models in edge-aware point cloud evaluation. Project page: https: //pixel-perfect-depth. github. io/.

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