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20172024
most citedEfficientFormer: Vision Transformers at MobileNet Speed

257 citations · 854 across the 72 of their papers we have counts for

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Showing 2022Show all

19 papers · 1 filter

cs.CV2022★ 1 cited

DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis

Yinghao Xu, Menglei Chai, Zifan Shi +8

Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of obj…

cs.CV2022

LADIS: Language Disentanglement for 3D Shape Editing

Ian Huang, Panos Achlioptas, Tianyi Zhang +3

Natural language interaction is a promising direction for democratizing 3D shape design. However, existing methods for text-driven 3D shape editing face challenges in producing dec…

cs.CV2022★ 21 cited

Rethinking Vision Transformers for MobileNet Size and Speed

Yanyu Li, Ju Hu, Yang Wen +5

With the success of Vision Transformers (ViTs) in computer vision tasks, recent arts try to optimize the performance and complexity of ViTs to enable efficient deployment on mobile…

cs.CV2022

Real-Time Neural Light Field on Mobile Devices

Junli Cao, Huan Wang, Pavlo Chemerys +6

Recent efforts in Neural Rendering Fields (NeRF) have shown impressive results on novel view synthesis by utilizing implicit neural representation to represent 3D scenes. Due to th…

cs.CV2022★ 5 cited

SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation

Yen-Chi Cheng, Hsin-Ying Lee, Sergey Tulyakov +2

In this work, we present a novel framework built to simplify 3D asset generation for amateur users. To enable interactive generation, our method supports a variety of input modalit…

cs.CV2022★ 1 cited

Make-A-Story: Visual Memory Conditioned Consistent Story Generation

Tanzila Rahman, Hsin-Ying Lee, Jian Ren +3

There has been a recent explosion of impressive generative models that can produce high quality images (or videos) conditioned on text descriptions. However, all such approaches re…