5 papers · 1 filter
Pushing Rendering Boundaries: Hard Gaussian Splatting
Qingshan Xu, Jiequan Cui, Xuanyu Yi +4
3D Gaussian Splatting (3DGS) has demonstrated impressive Novel View Synthesis (NVS) results in a real-time rendering manner. During training, it relies heavily on the average magni…
CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction
Yuan Zhou, Qingshan Xu, Jiequan Cui +4
Recently, large efforts have been made to design efficient linear-complexity visual Transformers. However, current linear attention models are generally unsuitable to be deployed i…
Few-shot NeRF by Adaptive Rendering Loss Regularization
Qingshan Xu, Xuanyu Yi, Jianyao Xu +3
Novel view synthesis with sparse inputs poses great challenges to Neural Radiance Field (NeRF). Recent works demonstrate that the frequency regularization of Positional Encoding (P…
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.…
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…