activity
20192024
most citedDynamic Refinement Network for Oriented and Densely Packed Object Detection

14 citations · 48 across the 10 of their papers we have counts for

collaborators
Showing cs.CVShow all

12 papers · 1 filter

cs.CV2024

Towards Unified 3D Hair Reconstruction from Single-View Portraits

Yujian Zheng, Yuda Qiu, Leyang Jin +5

Single-view 3D hair reconstruction is challenging, due to the wide range of shape variations among diverse hairstyles. Current state-of-the-art methods are specialized in recoverin…

cs.CV202412 cited

LGTM: Local-to-Global Text-Driven Human Motion Diffusion Model

Haowen Sun, Ruikun Zheng, Haibin Huang +3

In this paper, we introduce LGTM, a novel Local-to-Global pipeline for Text-to-Motion generation. LGTM utilizes a diffusion-based architecture and aims to address the challenge of…

cs.CV2024

Spatial and Surface Correspondence Field for Interaction Transfer

Zeyu Huang, Honghao Xu, Haibin Huang +3

In this paper, we introduce a new method for the task of interaction transfer. Given an example interaction between a source object and an agent, our method can automatically infer…

cs.CV20231 cited

Agents meet OKR: An Object and Key Results Driven Agent System with Hierarchical Self-Collaboration and Self-Evaluation

Yi Zheng, Chongyang Ma, Kanle Shi +1

In this study, we introduce the concept of OKR-Agent designed to enhance the capabilities of Large Language Models (LLMs) in task-solving. Our approach utilizes both self-collabora…

cs.CV20211 cited

Scene Synthesis via Uncertainty-Driven Attribute Synchronization

Haitao Yang, Zaiwei Zhang, Siming Yan +5

Developing deep neural networks to generate 3D scenes is a fundamental problem in neural synthesis with immediate applications in architectural CAD, computer graphics, as well as i…

cs.CV2021

HPNet: Deep Primitive Segmentation Using Hybrid Representations

Siming Yan, Zhenpei Yang, Chongyang Ma +3

This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape represented as a point cloud into primitive patches. The key to deep primitive segmentation is…