activity
20172025
most citedSuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Robotics

110 citations · 145 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CV2025

GTAD: Global Temporal Aggregation Denoising Learning for 3D Semantic Occupancy Prediction

Tianhao Li, Yang Li, Mengtian Li +2

Accurately perceiving dynamic environments is a fundamental task for autonomous driving and robotic systems. Existing methods inadequately utilize temporal information, relying mai…

cs.CV2025

GT2-GS: Geometry-aware Texture Transfer for Gaussian Splatting

Wenjie Liu, Zhongliang Liu, Junwei Shu +2

Transferring 2D textures onto complex 3D scenes plays a vital role in enhancing the efficiency and controllability of 3D multimedia content creation. However, existing 3D style tra…

cs.CV2025

TimeSoccer: An End-to-End Multimodal Large Language Model for Soccer Commentary Generation

Ling You, Wenxuan Huang, Xinni Xie +5

Soccer is a globally popular sporting event, typically characterized by long matches and distinctive highlight moments. Recent advances in Multimodal Large Language Models (MLLMs)…

cs.LG2024

Open-World Reinforcement Learning over Long Short-Term Imagination

Jiajian Li, Qi Wang, Yunbo Wang +4

Training visual reinforcement learning agents in a high-dimensional open world presents significant challenges. While various model-based methods have improved sample efficiency by…

cs.CV2024

FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion Models

Changgu Chen, Libing Yang, Xiaoyan Yang +4

In recent years, large-scale pre-trained diffusion models have demonstrated their outstanding capabilities in image and video generation tasks. However, existing models tend to pro…

cs.CV2024

ClothPPO: A Proximal Policy Optimization Enhancing Framework for Robotic Cloth Manipulation with Observation-Aligned Action Spaces

Libing Yang, Yang Li, Long Chen

Vision-based robotic cloth unfolding has made great progress recently. However, prior works predominantly rely on value learning and have not fully explored policy-based techniques…