2 citations · 2 across the 6 of their papers we have counts for
9 papers
A Unified Framework for Image-to-3D Part Generation via Variable Granularity
Qiwen Gu, Xin Zhou, Xiwu Chen +3
Controllable 3D part generation is pivotal for digital asset creation, yet existing methods lack the flexibility to accommodate diverse user workflows requiring varying levels of c…
BehaviorWorldGen: Closing the Loop between Action Models and World Simulators via Controllable Behavior-Aware Structured World Generation
Jiaqi Wang, Zhuo Zhang, Haining Guan +15
Modern driving action models are increasingly improved in a self-improvement loop, where a learned world simulator imagines future observations and the resulting data is fed back t…
ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation
Xiao Luo, Mingyang Du, Xin Zhou +5
High-fidelity 3D generation predominantly relies on scaling model capacity and data, which incurs prohibitive computational costs. This paradigm typically requires learning geometr…
HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation
Xin Zhou, Dingkang Liang, Xiwu Chen +4
Driving world models serve as a pivotal technology for autonomous driving by simulating environmental dynamics. However, existing approaches predominantly focus on future scene gen…
Less is Enough: Training-Free Video Diffusion Acceleration via Runtime-Adaptive Caching
Xin Zhou, Dingkang Liang, Kaijin Chen +7
Video generation models have demonstrated remarkable performance, yet their broader adoption remains constrained by slow inference speeds and substantial computational costs, prima…
DiST-4D: Disentangled Spatiotemporal Diffusion with Metric Depth for 4D Driving Scene Generation
Jiazhe Guo, Yikang Ding, Xiwu Chen +8
Current generative models struggle to synthesize dynamic 4D driving scenes that simultaneously support temporal extrapolation and spatial novel view synthesis (NVS) without per-sce…
HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation
Xin Zhou, Dingkang Liang, Sifan Tu +6
Driving World Models (DWMs) have become essential for autonomous driving by enabling future scene prediction. However, existing DWMs are limited to scene generation and fail to inc…
UniScene: Unified Occupancy-centric Driving Scene Generation
Bohan Li, Jiazhe Guo, Hongsi Liu +14
Generating high-fidelity, controllable, and annotated training data is critical for autonomous driving. Existing methods typically generate a single data form directly from a coars…
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving
Diankun Zhang, Guoan Wang, Runwen Zhu +15
End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system. Despite simplicity and clarity, the performance of end-to-end autonomous driv…