6 citations · 9 across the 18 of their papers we have counts for
18 papers
ST-Gen4D: Embedding 4D Spatiotemporal Cognition into World Model for 4D Generation
Haonan Wang, Hanyu Zhou, Tao Gu +1
Generative models have achieved success in producing apparently coherent 2D videos, but remain challenging in the physical world due to lack of 4D spatiotemporal scale. Typically,…
ST-: Structured SpatioTemporal VLA for Robotic Manipulation
Chuanhao Ma, Hanyu Zhou, Shihan Peng +3
Vision-language-action (VLA) models have achieved great success on general robotic tasks, but still face challenges in fine-grained spatiotemporal manipulation. Typically, existing…
NEC-Diff: Noise-Robust Event-RAW Complementary Diffusion for Seeing Motion in Extreme Darkness
Haoyue Liu, Jinghan Xu, Luxin Feng +4
High-quality imaging of dynamic scenes in extremely low-light conditions is highly challenging. Photon scarcity induces severe noise and texture loss, causing significant image deg…
Cog2Gen3D: Sculpturing 3D Semantic-Geometric Cognition for 3D Generation
Haonan Wang, Hanyu Zhou, Haoyue Liu +2
Generative models have achieved success in producing semantically plausible 2D images, but it remains challenging in 3D generation due to the absence of spatial geometry constraint…
Adapting Depth Anything to Adverse Imaging Conditions with Events
Shihan Peng, Yuyang Xiong, Hanyu Zhou +5
Robust depth estimation under dynamic and adverse lighting conditions is essential for robotic systems. Currently, depth foundation models, such as Depth Anything, achieve great su…
4D-VGGT: A General Foundation Model with SpatioTemporal Awareness for Dynamic Scene Geometry Estimation
Haonan Wang, Hanyu Zhou, Haoyue Liu +1
We investigate a challenging task of dynamic scene geometry estimation, which requires representing both spatial and temporal features. Typically, existing methods align the two fe…
Injecting Frame-Event Complementary Fusion into Diffusion for Optical Flow in Challenging Scenes
Haonan Wang, Hanyu Zhou, Haoyue Liu +1
Optical flow estimation has achieved promising results in conventional scenes but faces challenges in high-speed and low-light scenes, which suffer from motion blur and insufficien…
TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion
Haoyue Liu, Jinghan Xu, Yi Chang +4
Video frame interpolation (VFI) that leverages the bio-inspired event cameras as guidance has recently shown better performance and memory efficiency than the frame-based methods,…
Bridge Frame and Event: Common Spatiotemporal Fusion for High-Dynamic Scene Optical Flow
Hanyu Zhou, Haonan Wang, Haoyue Liu +3
High-dynamic scene optical flow is a challenging task, which suffers spatial blur and temporal discontinuous motion due to large displacement in frame imaging, thus deteriorating t…
Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation
Hanyu Zhou, Yi Chang, Zhiwei Shi +4
Optical flow has made great progress in clean scenes, while suffers degradation under adverse weather due to the violation of the brightness constancy and gradient continuity assum…
CoSEC: A Coaxial Stereo Event Camera Dataset for Autonomous Driving
Shihan Peng, Hanyu Zhou, Hao Dong +5
Conventional frame camera is the mainstream sensor of the autonomous driving scene perception, while it is limited in adverse conditions, such as low light. Event camera with high…
Seeing Motion at Nighttime with an Event Camera
Haoyue Liu, Shihan Peng, Lin Zhu +3
We focus on a very challenging task: imaging at nighttime dynamic scenes. Most previous methods rely on the low-light enhancement of a conventional RGB camera. However, they would…
JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object Detection
Hanyu Zhou, Zhiwei Shi, Hao Dong +3
Event-based moving object detection is a challenging task, where static background and moving object are mixed together. Typically, existing methods mainly align the background eve…
Bring Event into RGB and LiDAR: Hierarchical Visual-Motion Fusion for Scene Flow
Hanyu Zhou, Yi Chang, Zhiwei Shi +1
Single RGB or LiDAR is the mainstream sensor for the challenging scene flow, which relies heavily on visual features to match motion features. Compared with single modality, existi…
Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow
Hanyu Zhou, Yi Chang, Haoyue Liu +4
We investigate a challenging task of nighttime optical flow, which suffers from weakened texture and amplified noise. These degradations weaken discriminative visual features, thus…
Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow
Hanyu Zhou, Yi Chang, Gang Chen +1
Optical flow estimation has made great progress, but usually suffers from degradation under adverse weather. Although semi/full-supervised methods have made good attempts, the doma…
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow
Hanyu Zhou, Yi Chang, Wending Yan +1
Optical flow has achieved great success under clean scenes, but suffers from restricted performance under foggy scenes. To bridge the clean-to-foggy domain gap, the existing method…
Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translation
Yuntong Ye, Yi Chang, Hanyu Zhou +1
Existing deep learning-based image deraining methods have achieved promising performance for synthetic rainy images, typically rely on the pairs of sharp images and simulated rainy…