activity
20202025
most citedInteractive Segment Anything NeRF with Feature Imitation

9 citations · 11 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2025

DreamArt: Generating Interactable Articulated Objects from a Single Image

Ruijie Lu, Yu Liu, Jiaxiang Tang +6

Generating articulated objects, such as laptops and microwaves, is a crucial yet challenging task with extensive applications in Embodied AI and AR/VR. Current image-to-3D methods…

cs.CV2025

TACO: Taming Diffusion for in-the-wild Video Amodal Completion

Ruijie Lu, Yixin Chen, Yu Liu +5

Humans can infer complete shapes and appearances of objects from limited visual cues, relying on extensive prior knowledge of the physical world. However, completing partially obse…

cs.CV2024

Template-free Articulated Gaussian Splatting for Real-time Reposable Dynamic View Synthesis

Diwen Wan, Yuxiang Wang, Ruijie Lu +1

While novel view synthesis for dynamic scenes has made significant progress, capturing skeleton models of objects and re-posing them remains a challenging task. To tackle this prob…

cs.CV2024

MOVIS: Enhancing Multi-Object Novel View Synthesis for Indoor Scenes

Ruijie Lu, Yixin Chen, Junfeng Ni +5

Repurposing pre-trained diffusion models has been proven to be effective for NVS. However, these methods are mostly limited to a single object; directly applying such methods to co…

cs.CV2024

Superpoint Gaussian Splatting for Real-Time High-Fidelity Dynamic Scene Reconstruction

Diwen Wan, Ruijie Lu, Gang Zeng

Rendering novel view images in dynamic scenes is a crucial yet challenging task. Current methods mainly utilize NeRF-based methods to represent the static scene and an additional t…

cs.CV2023★ 9 cited

Interactive Segment Anything NeRF with Feature Imitation

Xiaokang Chen, Jiaxiang Tang, Diwen Wan +2

This paper investigates the potential of enhancing Neural Radiance Fields (NeRF) with semantics to expand their applications. Although NeRF has been proven useful in real-world app…

cs.CV2020★ 2 cited

Controllable Orthogonalization in Training DNNs

Lei Huang, Li Liu, Fan Zhu +4

Orthogonality is widely used for training deep neural networks (DNNs) due to its ability to maintain all singular values of the Jacobian close to 1 and reduce redundancy in represe…