activity
20242026
collaborators

8 papers

cs.CV2026

ClipGStream: Clip-Stream Gaussian Splatting for Any Length and Any Motion Multi-View Dynamic Scene Reconstruction

Jie Liang, Jiahao Wu, Chao Wang +7

Dynamic 3D scene reconstruction is essential for immersive media such as VR, MR, and XR, yet remains challenging for long multi-view sequences with large-scale motion. Existing dyn…

cs.CV2026

Intrinsic Geometry-Appearance Consistency Optimization for Sparse-View Gaussian Splatting

Kaiqiang Xiong, Rui Peng, Jiahao Wu +5

3D human reconstruction from a single image is a challenging problem and has been exclusively studied in the literature. Recently, some methods have resorted to diffusion models fo…

cs.CV2026

Multimodal-Prior-Guided Importance Sampling for Hierarchical Gaussian Splatting in Sparse-View Novel View Synthesis

Kaiqiang Xiong, Zhanke Wang, Ronggang Wang

We present multimodal-prior-guided importance sampling as the central mechanism for hierarchical 3D Gaussian Splatting (3DGS) in sparse-view novel view synthesis. Our sampler fuses…

cs.CV2025

LocalDyGS: Multi-view Global Dynamic Scene Modeling via Adaptive Local Implicit Feature Decoupling

Jiahao Wu, Rui Peng, Jianbo Jiao +7

Due to the complex and highly dynamic motions in the real world, synthesizing dynamic videos from multi-view inputs for arbitrary viewpoints is challenging. Previous works based on…

cs.CV2025

Swift4D:Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene

Jiahao Wu, Rui Peng, Zhiyan Wang +5

Novel view synthesis has long been a practical but challenging task, although the introduction of numerous methods to solve this problem, even combining advanced representations li…

cs.CV2025

CL-MVSNet: Unsupervised Multi-view Stereo with Dual-level Contrastive Learning

Kaiqiang Xiong, Rui Peng, Zhe Zhang +4

Unsupervised Multi-View Stereo (MVS) methods have achieved promising progress recently. However, previous methods primarily depend on the photometric consistency assumption, which…