3 citations · 6 across the 13 of their papers we have counts for
14 papers
GeoFlow: Efficient Driving Video Generation via Geometry-Aligned Priors
Jiazheng Liu, Hang Li, Jiawei Zhang +5
Generative models like Diffusion Models and Flow Matching have demonstrated remarkable capabilities in synthesizing high-fidelity driving videos, but are severely constrained by hi…
Learning Gaussian Structure: Intervention-Guided Density Control for Feed-Forward Driving Reconstruction
Hang Li, Jiahe Li, Meiying Gu +3
Feed-forward Gaussian reconstruction has recently emerged as an efficient approach for driving scene reconstruction. However, prevailing LiDAR-based methods preserve the initial co…
Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction
Jiahe Li, Jiawei Zhang, Xiao Bai +4
Surface reconstruction with differentiable rendering has achieved impressive performance in recent years, yet the pervasive photometric ambiguities have strictly bottlenecked exist…
SCE-SLAM: Scale-Consistent Monocular SLAM via Scene Coordinate Embeddings
Yuchen Wu, Jiahe Li, Xiaohan Yu +3
Monocular visual SLAM enables 3D reconstruction from internet video and autonomous navigation on resource-constrained platforms, yet suffers from scale drift, i.e., the gradual div…
FoundationSLAM: Unleashing the Power of Depth Foundation Models for End-to-End Dense Visual SLAM
Yuchen Wu, Jiahe Li, Fabio Tosi +3
We present FoundationSLAM, a learning-based monocular dense SLAM system that addresses the absence of geometric consistency in previous flow-based approaches for accurate and robus…
SparseSurf: Sparse-View 3D Gaussian Splatting for Surface Reconstruction
Meiying Gu, Jiawei Zhang, Jiahe Li +4
Recent advances in optimizing Gaussian Splatting for scene geometry have enabled efficient reconstruction of detailed surfaces from images. However, when input views are sparse, su…
GeoSVR: Taming Sparse Voxels for Geometrically Accurate Surface Reconstruction
Jiahe Li, Jiawei Zhang, Youmin Zhang +4
Reconstructing accurate surfaces with radiance fields has achieved remarkable progress in recent years. However, prevailing approaches, primarily based on Gaussian Splatting, are i…
EVPGS: Enhanced View Prior Guidance for Splatting-based Extrapolated View Synthesis
Jiahe Li, Feiyu Wang, Xiaochao Qu +3
Gaussian Splatting (GS)-based methods rely on sufficient training view coverage and perform synthesis on interpolated views. In this work, we tackle the more challenging and undere…
InsTaG: Learning Personalized 3D Talking Head from Few-Second Video
Jiahe Li, Jiawei Zhang, Xiao Bai +3
Despite exhibiting impressive performance in synthesizing lifelike personalized 3D talking heads, prevailing methods based on radiance fields suffer from high demands for training…
HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation
Wentian Qu, Jiahe Li, Jian Cheng +6
Understanding of bimanual hand-object interaction plays an important role in robotics and virtual reality. However, due to significant occlusions between hands and object as well a…
CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization
Jiawei Zhang, Jiahe Li, Xiaohan Yu +4
3D Gaussian Splatting (3DGS) creates a radiance field consisting of 3D Gaussians to represent a scene. With sparse training views, 3DGS easily suffers from overfitting, negatively…
TalkingGaussian: Structure-Persistent 3D Talking Head Synthesis via Gaussian Splatting
Jiahe Li, Jiawei Zhang, Xiao Bai +4
Radiance fields have demonstrated impressive performance in synthesizing lifelike 3D talking heads. However, due to the difficulty in fitting steep appearance changes, the prevaili…
DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization
Jiahe Li, Jiawei Zhang, Xiao Bai +4
Radiance fields have demonstrated impressive performance in synthesizing novel views from sparse input views, yet prevailing methods suffer from high training costs and slow infere…
Robust Synthetic-to-Real Transfer for Stereo Matching
Jiawei Zhang, Jiahe Li, Lei Huang +4
With advancements in domain generalized stereo matching networks, models pre-trained on synthetic data demonstrate strong robustness to unseen domains. However, few studies have in…