1 citations · 1 across the 10 of their papers we have counts for
11 papers
DreamStream: Towards Policy-Oriented Generative Simulation for End-to-End Driving
Ziyang Leng, Sicheng Mo, Seth Z. Zhao +4
Faithfully evaluating end-to-end driving policies in simulation requires observations that are not merely photo-realistic, but preserve the scene features a policy relies on to mak…
NavSafe-: Benchmarking Closed-Loop Driving Safety in Photorealistic Environments
Yuxin Bao, Hongwei Ruan, Luobin Wang +7
End-to-end (E2E) driving policies have advanced rapidly on open-loop (OL) benchmarks, yet OL evaluation cannot reveal whether a policy can withstand compounding errors, recover fro…
Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers
Sicheng Mo, Yuheng Li, Ziyang Leng +2
Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing a…
Group Diffusion: Enhancing Image Generation by Unlocking Cross-Sample Collaboration
Sicheng Mo, Thao Nguyen, Richard Zhang +7
In this work, we explore an untapped signal in diffusion model inference. While all previous methods generate images independently at inference, we instead ask if samples can be ge…
Occupancy Learning with Spatiotemporal Memory
Ziyang Leng, Jiawei Yang, Wenlong Yi +1
3D occupancy becomes a promising perception representation for autonomous driving to model the surrounding environment at a fine-grained scale. However, it remains challenging to e…
BEVCon: Advancing Bird's Eye View Perception with Contrastive Learning
Ziyang Leng, Jiawei Yang, Zhicheng Ren +1
We present BEVCon, a simple yet effective contrastive learning framework designed to improve Bird's Eye View (BEV) perception in autonomous driving. BEV perception offers a top-dow…
Dreamland: Controllable World Creation with Simulator and Generative Models
Sicheng Mo, Ziyang Leng, Leon Liu +3
Large-scale video generative models can synthesize diverse and realistic visual content for dynamic world creation, but they often lack element-wise controllability, hindering thei…
X-Fusion: Introducing New Modality to Frozen Large Language Models
Sicheng Mo, Thao Nguyen, Xun Huang +9
We propose X-Fusion, a framework that extends pretrained Large Language Models (LLMs) for multimodal tasks while preserving their language capabilities. X-Fusion employs a dual-tow…
SimGen: Simulator-conditioned Driving Scene Generation
Yunsong Zhou, Michael Simon, Zhenghao Peng +4
Controllable synthetic data generation can substantially lower the annotation cost of training data. Prior works use diffusion models to generate driving images conditioned on the…
Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance
Kuan Heng Lin, Sicheng Mo, Ben Klingher +2
Recent controllable generation approaches such as FreeControl and Diffusion Self-Guidance bring fine-grained spatial and appearance control to text-to-image (T2I) diffusion models…
FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Condition
Sicheng Mo, Fangzhou Mu, Kuan Heng Lin +4
Recent approaches such as ControlNet offer users fine-grained spatial control over text-to-image (T2I) diffusion models. However, auxiliary modules have to be trained for each type…