42 citations · 51 across the 19 of their papers we have counts for
20 papers
Temporally Grounded Compositional Camera Motion Understanding via Geometric Knowledge Distillation
Dazhao Du, Shiyan Du, Jian Liu +8
Understanding camera motion is fundamental to video perception, with applications in spatial intelligence and controllable video generation. Multimodal large language models (MLLMs…
WorldCycle: Self-Verifiable Reinforcement Learning for Long-Horizon Video World Models
Bohai Gu, Yueyang Yuan, Taiyi Wu +9
Interactive video world models are essential for long-horizon planning and exploration, yet they suffer from compounding errors. Post-training methods such as reinforcement learnin…
Train the Agent, Not the Expert: Learning to Harness Heterogeneous Experts for Multi-Turn Visual Reasoning
Yaowu Fan, Tao Han, Dazhao Du +2
Recent progress in computer vision has produced a wide range of powerful specialized models for detection, segmentation, counting, and other visual tasks. However, these models are…
WorldCraft: From Camera Navigation to Object Manipulation in Interactive Video World Models
Bohai Gu, Taiyi Wu, Yueyang Yuan +9
Recent video world models enable interactive camera navigation but provide limited control over individual objects. We study persistent object-state control in autoregressive video…
Learning Spatiotemporal Sensitivity in Video LLMs via Counterfactual Reinforcement Learning
Dazhao Du, Jian Liu, Jialong Qin +7
Video large language models (Video LLMs) can achieve strong video-QA accuracy without reliably tracking spatiotemporal dynamics. A model may answer a motion question from static cu…
MLLMs Know When Before Speaking: Revealing and Recovering Temporal Grounding via Attention Cues
Dazhao Du, Liao Duan, Jian Liu +5
Video temporal grounding (VTG), which localizes the start and end times of a queried event in an untrimmed video, is a key test of whether multimodal large language models (MLLMs)…
From Visual Synthesis to Interactive Worlds: Toward Production-Ready 3D Asset Generation
Jiafeng Wu, Zhuofan Lou, Jian Liu +3
Three-dimensional content generation has progressed from producing isolated, visually plausible shapes to constructing structured assets that can be deployed in real-time interacti…
3D Generation for Embodied AI and Robotic Simulation: A Survey
Tianwei Ye, Yifan Mao, Minwen Liao +6
Embodied AI and robotic systems increasingly depend on scalable, diverse, and physically grounded 3D content for simulation-based training and real-world deployment. While 3D gener…
GUI Agents with Reinforcement Learning: Toward Digital Inhabitants
Junan Hu, Jian Liu, Jingxiang Lai +6
Graphical User Interface (GUI) agents have emerged as a promising paradigm for intelligent systems that perceive and interact with graphical interfaces visually. Yet supervised fin…
GrandGuard: Taxonomy, Benchmark, and Safeguards for Elderly-Chatbot Interaction Safety
Changxuan Fan, Xi Yang, Yueyuan Zheng +9
As older adults increasingly use LLM-based chatbots for companionship and assistance, a safety gap is emerging. Older adults may face vulnerabilities from social isolation, limited…
Place-it-R1: Unlocking Environment-aware Reasoning Potential of MLLM for Video Object Insertion
Bohai Gu, Taiyi Wu, Dazhao Du +5
Video object insertion is fundamental to video editing, yet existing diffusion methods often produce visually plausible but physically inconsistent results. We present Place-it-R1,…
Mesh-Pro: Asynchronous Advantage-guided Ranking Preference Optimization for Artist-style Quadrilateral Mesh Generation
Zhen Zhou, Jian Liu, Biwen Lei +10
Reinforcement learning (RL) has demonstrated remarkable success in text and image generation, yet its potential in 3D generation remains largely unexplored. Existing attempts typic…
Predicting the Future by Retrieving the Past
Dazhao Du, Tao Han, Song Guo
Deep learning models such as MLP, Transformer, and TCN have achieved remarkable success in univariate time series forecasting, typically relying on sliding window samples from hist…
A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation
Dazhao Du, Lingyu Si, Fanjiang Xu +2
Due to the selective absorption and scattering of light by diverse aquatic media, underwater images usually suffer from various visual degradations. Existing underwater image enhan…
End-To-End Underwater Video Enhancement: Dataset and Model
Dazhao Du, Enhan Li, Lingyu Si +2
Underwater video enhancement (UVE) aims to improve the visibility and frame quality of underwater videos, which has significant implications for marine research and exploration. Ho…
UIEDP:Underwater Image Enhancement with Diffusion Prior
Dazhao Du, Enhan Li, Lingyu Si +3
Underwater image enhancement (UIE) aims to generate clear images from low-quality underwater images. Due to the unavailability of clear reference images, researchers often synthesi…
Timestamp-Supervised Action Segmentation from the Perspective of Clustering
Dazhao Du, Enhan Li, Lingyu Si +2
Video action segmentation under timestamp supervision has recently received much attention due to lower annotation costs. Most existing methods generate pseudo-labels for all frame…
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
Bing Su, Dazhao Du, Zhao Yang +6
Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive abi…
Do we really need temporal convolutions in action segmentation?
Dazhao Du, Bing Su, Yu Li +3
Action classification has made great progress, but segmenting and recognizing actions from long untrimmed videos remains a challenging problem. Most state-of-the-art methods focus…
Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting
Dazhao Du, Bing Su, Zhewei Wei
Transformer-based methods have shown great potential in long-term time series forecasting. However, most of these methods adopt the standard point-wise self-attention mechanism, wh…