5 papers
HiFiVe: High-Fidelity Vehicle Generation Leveraging Auto-Regressive 2D Generative Priors
Hongli Xiao, Youjian Zhang, Qi Zheng +5
Existing 3D vehicle generation methods often suffer from low geometric fidelity and blurry textures, hindering their downstream applications. While recent works adopt multi-view di…
3DCarGen: Scalable 3D Car Generation via 3D-consistent Multi-view Synthesis
Hongli Xiao, Youjian Zhang, Yaohui Jin +3
High-quality 3D vehicle assets are essential for autonomous driving simulation. Although multi-view diffusion-based paradigms enable controllable single-image reconstruction, they…
MM-TRELLIS: Point-Cloud Guided Multi-Modal 3D Vehicle Generation in Autonomous Driving
Hongli Xiao, Youjian Zhang, Yucai Bai +5
Recovering realistic 3D vehicle models from autonomous driving scenes is crucial for synthesizing training data and building simulation environment. However, most existing vehicle…
Skeleton2Stage: Reward-Guided Fine-Tuning for Physically Plausible Dance Generation
Jidong Jia, Youjian Zhang, Huan Fu +1
Despite advances in dance generation, most methods are trained in the skeletal domain and ignore mesh-level physical constraints. As a result, motions that look plausible as joint…
DGS: Dense Depth Regularization for LiDAR-free Urban Scene Reconstruction
Kejing Xia, Jidong Jia, Ke Jin +4
Recently, Gaussian Splatting (GS) has shown great potential for urban scene reconstruction in the field of autonomous driving. However, current urban scene reconstruction methods o…