7 papers · 1 filter
: 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…
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…
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…
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.…
Large Point-to-Gaussian Model for Image-to-3D Generation
Longfei Lu, Huachen Gao, Tao Dai +4
Recently, image-to-3D approaches have significantly advanced the generation quality and speed of 3D assets based on large reconstruction models, particularly 3D Gaussian reconstruc…