7 papers
: 3D Reconstruction via Relative Regression
Congrong Xu, Huachen Gao, Xingyu Chen +3
Recent feed-forward geometry foundation models have demonstrated impressive generalization by recovering depth and poses in a single forward pass. However, these models are typical…
You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale
Baorui Ma, Huachen Gao, Haoge Deng +4
Recent 3D generation models typically rely on limited-scale 3D `gold-labels' or 2D diffusion priors for 3D content creation. However, their performance is upper-bounded by constrai…
MVD-HuGaS: Human Gaussians from a Single Image via 3D Human Multi-view Diffusion Prior
Kaiqiang Xiong, Ying Feng, Qi Zhang +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…
Disentangled Generation and Aggregation for Robust Radiance Fields
Shihe Shen, Huachen Gao, Wangze Xu +5
The utilization of the triplane-based radiance fields has gained attention in recent years due to its ability to effectively disentangle 3D scenes with a high-quality representatio…
MVPGS: Excavating Multi-view Priors for Gaussian Splatting from Sparse Input Views
Wangze Xu, Huachen Gao, Shihe Shen +3
Recently, the Neural Radiance Field (NeRF) advancement has facilitated few-shot Novel View Synthesis (NVS), which is a significant challenge in 3D vision applications. Despite nume…
Surface-Centric Modeling for High-Fidelity Generalizable Neural Surface Reconstruction
Rui Peng, Shihe Shen, Kaiqiang Xiong +4
Reconstructing the high-fidelity surface from multi-view images, especially sparse images, is a critical and practical task that has attracted widespread attention in recent years.…