4 citations · 6 across the 20 of their papers we have counts for
22 papers
Benchmark Everything Everywhere All at Once
Shiyun Xiong, Dongming Wu, Peiwen Sun +5
Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance. However, their construction is labor-intensiv…
Bridging Scene Generation and Planning: Driving with World Model via Unifying Vision and Motion Representation
Xingtai Gui, Meijie Zhang, Tianyi Yan +5
End-to-end autonomous driving aims to generate safe and plausible planning policies from raw sensor input. Driving world models have shown great potential in learning rich represen…
HanMoVLM: Large Vision-Language Models for Professional Artistic Painting Evaluation
Hongji Yang, Yucheng Zhou, Wencheng Han +3
While Large Vision-Language Models (VLMs) demonstrate impressive general visual capabilities, they remain artistically blind and unable to offer professional evaluation of artworks…
Towards Geometry-Aware and Motion-Guided Video Human Mesh Recovery
Hongjun Chen, Huan Zheng, Wencheng Han +1
Existing video-based 3D Human Mesh Recovery (HMR) methods often produce physically implausible results, stemming from their reliance on flawed intermediate 3D pose anchors and thei…
From Human Intention to Action Prediction: Intention-Driven End-to-End Autonomous Driving
Huan Zheng, Yucheng Zhou, Tianyi Yan +9
While end-to-end autonomous driving has achieved remarkable progress in geometric control, current systems remain constrained by a command-following paradigm that relies on simple…
TrajDiff: End-to-end Autonomous Driving without Perception Annotation
Xingtai Gui, Jianbo Zhao, Wencheng Han +5
End-to-end autonomous driving systems directly generate driving policies from raw sensor inputs. While these systems can extract effective environmental features for planning, rely…
HiCoGen: Hierarchical Compositional Text-to-Image Generation in Diffusion Models via Reinforcement Learning
Hongji Yang, Yucheng Zhou, Wencheng Han +4
Recent advances in diffusion models have demonstrated impressive capability in generating high-quality images for simple prompts. However, when confronted with complex prompts invo…
AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models
Tianyi Yan, Tao Tang, Xingtai Gui +11
End-to-end models for autonomous driving hold the promise of learning complex behaviors directly from sensor data, but face critical challenges in safety and handling long-tail eve…
RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generation
Tianyi Yan, Wencheng Han, Xia Zhou +4
Synthetic data is crucial for advancing autonomous driving (AD) systems, yet current state-of-the-art video generation models, despite their visual realism, suffer from subtle geom…
Towards Better Cephalometric Landmark Detection with Diffusion Data Generation
Dongqian Guo, Wencheng Han, Pang Lyu +2
Cephalometric landmark detection is essential for orthodontic diagnostics and treatment planning. Nevertheless, the scarcity of samples in data collection and the extensive effort…
Self-Rewarding Large Vision-Language Models for Optimizing Prompts in Text-to-Image Generation
Hongji Yang, Yucheng Zhou, Wencheng Han +1
Text-to-image models are powerful for producing high-quality images based on given text prompts, but crafting these prompts often requires specialized vocabulary. To address this,…
Breaking Down Monocular Ambiguity: Exploiting Temporal Evolution for 3D Lane Detection
Huan Zheng, Wencheng Han, Tianyi Yan +2
Monocular 3D lane detection aims to estimate the 3D position of lanes from frontal-view (FV) images. However, existing methods are fundamentally constrained by the inherent ambigui…
Reducing CT Metal Artifacts by Learning Latent Space Alignment with Gemstone Spectral Imaging Data
Wencheng Han, Dongqian Guo, Xiao Chen +3
Metal artifacts in CT slices have long posed challenges in medical diagnostics. These artifacts degrade image quality, resulting in suboptimal visualization and complicating the ac…
DC-ControlNet: Decoupling Inter- and Intra-Element Conditions in Image Generation with Diffusion Models
Hongji Yang, Wencheng Han, Yucheng Zhou +1
In this paper, we introduce DC (Decouple)-ControlNet, a highly flexible and precisely controllable framework for multi-condition image generation. The core idea behind DC-ControlNe…
RAWMamba: Unified sRGB-to-RAW De-rendering With State Space Model
Hongjun Chen, Wencheng Han, Huan Zheng +1
Recent advancements in sRGB-to-RAW de-rendering have increasingly emphasized metadata-driven approaches to reconstruct RAW data from sRGB images, supplemented by partial RAW inform…
DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation
Tianyi Yan, Dongming Wu, Wencheng Han +5
Autonomous driving evaluation requires simulation environments that closely replicate actual road conditions, including real-world sensory data and responsive feedback loops. Howev…
Towards High-Fidelity 3D Portrait Generation with Rich Details by Cross-View Prior-Aware Diffusion
Haoran Wei, Wencheng Han, Xingping Dong +1
Recent diffusion-based Single-image 3D portrait generation methods typically employ 2D diffusion models to provide multi-view knowledge, which is then distilled into 3D representat…
Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-Resolution
Huan Zheng, Wencheng Han, Jianbing Shen
Recovering high-quality depth maps from compressed sources has gained significant attention due to the limitations of consumer-grade depth cameras and the bandwidth restrictions du…
RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception
Chunliang Li, Wencheng Han, Junbo Yin +2
Concurrent processing of multiple autonomous driving 3D perception tasks within the same spatiotemporal scene poses a significant challenge, in particular due to the computational…
Referring Multi-Object Tracking with Diverse Dynamic Expressions
Yani Zhang, Dongming Wu, Wencheng Han +3
Referring understanding is a fundamental task that bridges natural language and visual content by localizing objects described in free-form expressions. However, existing works are…
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving
Wencheng Han, Dongqian Guo, Cheng-Zhong Xu +1
In the field of autonomous driving, two important features of autonomous driving car systems are the explainability of decision logic and the accuracy of environmental perception.…
Multi-Resolution Fusion for Fully Automatic Cephalometric Landmark Detection
Dongqian Guo, Wencheng Han
Cephalometric landmark detection on lateral skull X-ray images plays a crucial role in the diagnosis of certain dental diseases. Accurate and effective identification of these land…