most citedAdaptive-avg-pooling based Attention Vision Transformer for Face Anti-spoofing

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.GR2025

FlexPainter: Flexible and Multi-View Consistent Texture Generation

Dongyu Yan, Leyi Wu, Jiantao Lin +7

Texture map production is an important part of 3D modeling and determines the rendering quality. Recently, diffusion-based methods have opened a new way for texture generation. How…

cs.GR2025

Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation

Jiantao Lin, Xin Yang, Meixi Chen +7

Diffusion models have achieved great success in generating 2D images. However, the quality and generalizability of 3D content generation remain limited. State-of-the-art methods of…

cs.CV2024

MultiGO: Towards Multi-level Geometry Learning for Monocular 3D Textured Human Reconstruction

Gangjian Zhang, Nanjie Yao, Shunsi Zhang +4

This paper investigates the research task of reconstructing the 3D clothed human body from a monocular image. Due to the inherent ambiguity of single-view input, existing approache…

cs.CV2024

Human Multi-View Synthesis from a Single-View Model:Transferred Body and Face Representations

Yu Feng, Shunsi Zhang, Jian Shu +4

Generating multi-view human images from a single view is a complex and significant challenge. Although recent advancements in multi-view object generation have shown impressive res…

eess.AS2024

Debatts: Zero-Shot Debating Text-to-Speech Synthesis

Yiqiao Huang, Yuancheng Wang, Jiaqi Li +4

In debating, rebuttal is one of the most critical stages, where a speaker addresses the arguments presented by the opposing side. During this process, the speaker synthesizes their…

eess.IV20241 cited

Adaptive-avg-pooling based Attention Vision Transformer for Face Anti-spoofing

Jichen Yang, Fangfan Chen, Rohan Kumar Das +2

Traditional vision transformer consists of two parts: transformer encoder and multi-layer perception (MLP). The former plays the role of feature learning to obtain better represent…