activity
20192026
most citedSecure Federated Submodel Learning

28 citations · 88 across the 18 of their papers we have counts for

collaborators

19 papers

hep-th2026

Holographic theory of non-invertible gravitational anomalies

Xiao-Gang Wen

We review a many-body formulation of gravitational anomalies as obstructions to realizing the complete low-energy data by a local {\it bosonic} lattice quantum system of the same d…

cs.CV2026

Hitem3D 2.0: Multi-View Guided Native 3D Texture Generation

Huiang He, Shengchu Zhao, Jianwen Huang +7

Although recent advances have improved the quality of 3D texture generation, existing methods still struggle with incomplete texture coverage, cross-view inconsistency, and misalig…

cs.CV2026

EI-Part: Explode for Completion and Implode for Refinement

Wanhu Sun, Zhongjin Luo, Heliang Zheng +6

Part-level 3D generation is crucial for various downstream applications, including gaming, film production, and industrial design. However, decomposing a 3D shape into geometricall…

cs.CV2026

Know3D: Prompting 3D Generation with Knowledge from Vision-Language Models

Wenyue Chen, Wenjue Chen, Peng Li +6

Recent advances in 3D generation have improved the fidelity and geometric details of synthesized 3D assets. However, due to the inherent ambiguity of single-view observations and t…

cs.CV2025★ 1 cited

Topology-Preserved Auto-regressive Mesh Generation in the Manner of Weaving Silk

Gaochao Song, Zibo Zhao, Haohan Weng +3

Existing auto-regressive mesh generation approaches suffer from ineffective topology preservation, which is crucial for practical applications. This limitation stems from previous…

cs.CV2025

Hyper3D: Efficient 3D Representation via Hybrid Triplane and Octree Feature for Enhanced 3D Shape Variational Auto-Encoders

Jingyu Guo, Sensen Gao, Jia-Wang Bian +4

Recent 3D content generation pipelines often leverage Variational Autoencoders (VAEs) to encode shapes into compact latent representations, facilitating diffusion-based generation.…

cs.CV2023

NeuDA: Neural Deformable Anchor for High-Fidelity Implicit Surface Reconstruction

Bowen Cai, Jinchi Huang, Rongfei Jia +2

This paper studies implicit surface reconstruction leveraging differentiable ray casting. Previous works such as IDR and NeuS overlook the spatial context in 3D space when predicti…

cs.CV2022

3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue

Bowen Cai, Yujie Li, Yuqin Liang +4

This paper studies how to flexibly integrate reconstructed 3D models into practical 3D modeling pipelines such as 3D scene creation and rendering. Due to the technical difficulty,…

cs.CV2022★ 5 cited

DCCF: Deep Comprehensible Color Filter Learning Framework for High-Resolution Image Harmonization

Ben Xue, Shenghui Ran, Quan Chen +3

Image color harmonization algorithm aims to automatically match the color distribution of foreground and background images captured in different conditions. Previous deep learning…

cs.CV2022

Ray Priors through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation

Jian Zhang, Yuanqing Zhang, Huan Fu +6

Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images. A main limitation of conventional NeRFs is that the…

cs.GR2022★ 5 cited

NeRF-Editing: Geometry Editing of Neural Radiance Fields

Yu-Jie Yuan, Yang-Tian Sun, Yu-Kun Lai +3

Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable…

cs.CV2022★ 7 cited

Modeling Indirect Illumination for Inverse Rendering

Yuanqing Zhang, Jiaming Sun, Xingyi He +3

Recent advances in implicit neural representations and differentiable rendering make it possible to simultaneously recover the geometry and materials of an object from multi-view R…

cs.LG2021★ 1 cited

Data-Free Evaluation of User Contributions in Federated Learning

Hongtao Lv, Zhenzhe Zheng, Tie Luo +5

Federated learning (FL) trains a machine learning model on mobile devices in a distributed manner using each device's private data and computing resources. A critical issues is to…

cs.CV2021

Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation

Bowen Cai, Huan Fu, Rongfei Jia +3

Adapting semantic segmentation models to new domains is an important but challenging problem. Recently enlightening progress has been made, but the performance of existing methods…

cs.CV2020★ 15 cited

3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics

Huan Fu, Bowen Cai, Lin Gao +7

We introduce 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics), a new, large-scale, and comprehensive repository of synthetic indoor scenes highlighted by professionally des…

cs.CV2020★ 2 cited

Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning

Huan Fu, Shunming Li, Rongfei Jia +3

Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database. The common routine is to map 2D images and 3D shap…

cs.CV2020★ 16 cited

3D-FUTURE: 3D Furniture shape with TextURE

Huan Fu, Rongfei Jia, Lin Gao +4

The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories. Thus, they typically have insufficient geometric details and less informative textur…

cs.LG2020★ 8 cited

Distributed Optimization over Block-Cyclic Data

Yucheng Ding, Chaoyue Niu, Yikai Yan +5

We consider practical data characteristics underlying federated learning, where unbalanced and non-i.i.d. data from clients have a block-cyclic structure: each cycle contains sever…

cs.LG2019★ 28 cited

Secure Federated Submodel Learning

Chaoyue Niu, Fan Wu, Shaojie Tang +5

Federated learning was proposed with an intriguing vision of achieving collaborative machine learning among numerous clients without uploading their private data to a cloud server.…