no code implementations • 19 Aug 2024 • Minghua Liu, Chong Zeng, Xinyue Wei, Ruoxi Shi, Linghao Chen, Chao Xu, Mengqi Zhang, Zhaoning Wang, Xiaoshuai Zhang, Isabella Liu, Hongzhi Wu, Hao Su
The input normal maps can be predicted by 2D diffusion models, significantly aiding in the guidance and refinement of the geometry's learning.
no code implementations • 19 Aug 2024 • Chao Xu, Ang Li, Linghao Chen, Yulin Liu, Ruoxi Shi, Hao Su, Minghua Liu
The diffusion model is trained to jointly predict surrogate representations for camera poses and multi-view images of the object under known poses, integrating all information from the input sparse views.
no code implementations • 27 May 2024 • Qiang Wang, Minghua Liu, Junjun Hu, Fan Jiang, Mu Xu
In contrast, we propose an efficient long-duration video generation method based on noise reschedule specifically tailored for image animation tasks, facilitating the creation of videos over 100 frames in length while maintaining consistency in content scenery and motion coordination.
no code implementations • 25 Apr 2024 • Tongzhou Mu, Minghua Liu, Hao Su
The success of many RL techniques heavily relies on human-engineered dense rewards, which typically demand substantial domain expertise and extensive trial and error.
1 code implementation • 5 Dec 2023 • Yuchen Zhou, Jiayuan Gu, Xuanlin Li, Minghua Liu, Yunhao Fang, Hao Su
Open-world 3D part segmentation is pivotal in diverse applications such as robotics and AR/VR.
no code implementations • CVPR 2024 • Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, Hao Su
Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts.
1 code implementation • 23 Oct 2023 • Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, Hao Su
We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view.
1 code implementation • ICCV 2023 • Xuanlin Li, Yunhao Fang, Minghua Liu, Zhan Ling, Zhuowen Tu, Hao Su
Model distillation, the process of creating smaller, faster models that maintain the performance of larger models, is a promising direction towards the solution.
1 code implementation • NeurIPS 2023 • Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, Hao Su
Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world.
1 code implementation • NeurIPS 2023 • Minghua Liu, Ruoxi Shi, Kaiming Kuang, Yinhao Zhu, Xuanlin Li, Shizhong Han, Hong Cai, Fatih Porikli, Hao Su
Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.
Ranked #5 on
Zero-shot 3D Point Cloud Classification
on OmniObject3D (Pretrained on ShapeNet)
(using extra training data)
2 code implementations • CVPR 2023 • Minghua Liu, Yinhao Zhu, Hong Cai, Shizhong Han, Zhan Ling, Fatih Porikli, Hao Su
Generalizable 3D part segmentation is important but challenging in vision and robotics.
1 code implementation • 14 Oct 2022 • Minghua Liu, Xuanlin Li, Zhan Ling, Yangyan Li, Hao Su
We study how choices of input point cloud coordinate frames impact learning of manipulation skills from 3D point clouds.
no code implementations • 14 Oct 2022 • Minghua Liu, Yin Zhou, Charles R. Qi, Boqing Gong, Hao Su, Dragomir Anguelov
Our method co-designs an efficient labeling process with semi/weakly supervised learning and is applicable to nearly any 3D semantic segmentation backbones.
1 code implementation • 5 May 2022 • Xinyue Wei, Minghua Liu, Zhan Ling, Hao Su
Approximate convex decomposition aims to decompose a 3D shape into a set of almost convex components, whose convex hulls can then be used to represent the input shape.
1 code implementation • CVPR 2021 • Minghua Liu, Minhyuk Sung, Radomir Mech, Hao Su
Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles.
1 code implementation • ECCV 2020 • Minghua Liu, Xiaoshuai Zhang, Hao Su
We are interested in reconstructing the mesh representation of object surfaces from point clouds.
1 code implementation • CVPR 2020 • Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, Hao Su
To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable.
2 code implementations • 30 Nov 2019 • Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, Shi-Min Hu
3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community.
Ranked #8 on
Point Cloud Completion
on ShapeNet
no code implementations • 25 Sep 2019 • Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek, Hao Su, Henrik Iskov Christensen
Combining ideas from Batch RL and Meta RL, we propose tiMe, which learns distillation of multiple value functions and MDP embeddings from only existing data.
no code implementations • NeurIPS 2020 • Jiachen Li, Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek, Keith Ross, Henrik Iskov Christensen, Hao Su
To perform well, the policy must infer the task identity from collected transitions by modelling its dependency on states, actions and rewards.