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cs.CV2025
A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation
Wentao Qu, Guofeng Mei, Yang Wu +3
Text-to-LiDAR generation can customize 3D data with rich structures and diverse scenes for downstream tasks. However, the scarcity of Text-LiDAR pairs often causes insufficient tra…
cs.CV2025
Shortcutting Pre-trained Flow Matching Diffusion Models is Almost Free Lunch
Xu Cai, Yang Wu, Qianli Chen +3
We present an ultra-efficient post-training method for shortcutting large-scale pre-trained flow matching diffusion models into efficient few-step samplers, enabled by novel veloci…
cs.CV2025
Veila: Panoramic LiDAR Generation from a Monocular RGB Image
Youquan Liu, Lingdong Kong, Weidong Yang +8
Realistic and controllable panoramic LiDAR data generation is critical for scalable 3D perception in autonomous driving and robotics. Existing methods either perform unconditional…