1 citations · 1 across the 10 of their papers we have counts for
12 papers
GAD: Geometry Attention Distillation for Consistent Video Generation
Junfeng Ni, Ziyu Zhu, Fan Jiang +7
Video diffusion models generate visually compelling short-term sequences but often struggle to preserve scene geometry and object structure over time. In this paper, we first demon…
UniFusion: Sparse-View 4D Reconstruction via Unified Spatio-temporal Depth Alignment
Yongzhe Lyu, Shaofei Wang, Yixin Chen +1
In this paper, we address the challenging problem of 4D reconstruction from sparse-view videos. This setup usually relies on monocular depth estimation to provide priors for the re…
UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation
Haopeng Chen, Yihao Ai, Kabeen Kim +3
Low-visibility scenarios, such as low-light conditions, pose significant challenges to human pose estimation due to the scarcity of annotated low-light datasets and the loss of vis…
Hyperbolic Multiview Pretraining for Robotic Manipulation
Jin Yang, Ping Wei, Yixin Chen +1
3D-aware visual pretraining has proven effective in improving the performance of downstream robotic manipulation tasks. However, existing methods are constrained to Euclidean embed…
GaussianFluent: Gaussian Simulation for Dynamic Scenes with Mixed Materials
Bei Huang, Yixin Chen, Ruijie Lu +4
3D Gaussian Splatting (3DGS) has emerged as a prominent 3D representation for high-fidelity and real-time rendering. Prior work has coupled physics simulation with Gaussians, but p…
3D Scene Change Modeling With Consistent Multi-View Aggregation
Zirui Zhou, Junfeng Ni, Shujie Zhang +2
Change detection plays a vital role in scene monitoring, exploration, and continual reconstruction. Existing 3D change detection methods often exhibit spatial inconsistency in the…
G4Splat: Geometry-Guided Gaussian Splatting with Generative Prior
Junfeng Ni, Yixin Chen, Zhifei Yang +4
Despite recent advances in leveraging generative prior from pre-trained diffusion models for 3D scene reconstruction, existing methods still face two critical limitations. First, d…
GWM: Towards Scalable Gaussian World Models for Robotic Manipulation
Guanxing Lu, Baoxiong Jia, Puhao Li +4
Training robot policies within a learned world model is trending due to the inefficiency of real-world interactions. The established image-based world models and policies have show…
Trace3D: Consistent Segmentation Lifting via Gaussian Instance Tracing
Hongyu Shen, Junfeng Ni, Yixin Chen +3
We address the challenge of lifting 2D visual segmentation to 3D in Gaussian Splatting. Existing methods often suffer from inconsistent 2D masks across viewpoints and produce noisy…
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…
Decompositional Neural Scene Reconstruction with Generative Diffusion Prior
Junfeng Ni, Yu Liu, Ruijie Lu +4
Decompositional reconstruction of 3D scenes, with complete shapes and detailed texture of all objects within, is intriguing for downstream applications but remains challenging, par…
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…