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cs.CV2025

StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated Video Streams

Zike Wu, Qi Yan, Xuanyu Yi +2

Real-time reconstruction of dynamic 3D scenes from uncalibrated video streams demands robust online methods that recover scene dynamics from sparse observations under strict latenc…

cs.CV2024

MVGamba: Unify 3D Content Generation as State Space Sequence Modeling

Xuanyu Yi, Zike Wu, Qiuhong Shen +6

Recent 3D large reconstruction models (LRMs) can generate high-quality 3D content in sub-seconds by integrating multi-view diffusion models with scalable multi-view reconstructors.…

cs.CV2024

Diffusion Time-step Curriculum for One Image to 3D Generation

Xuanyu Yi, Zike Wu, Qingshan Xu +3

Score distillation sampling~(SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a \textbf{single} image. It leverages pre-traine…

cs.CV2024

Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction

Qiuhong Shen, Zike Wu, Xuanyu Yi +4

We tackle the challenge of efficiently reconstructing a 3D asset from a single image at millisecond speed. Existing methods for single-image 3D reconstruction are primarily based o…

cs.CV2024

Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior

Zike Wu, Pan Zhou, Xuanyu Yi +2

Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation, but are vulnerable to geometry collapse and poor textures yet. To…

cs.CV2023

Fast Diffusion Model

Zike Wu, Pan Zhou, Kenji Kawaguchi +1

Diffusion models (DMs) have been adopted across diverse fields with its remarkable abilities in capturing intricate data distributions. In this paper, we propose a Fast Diffusion M…