1 citations · 2 across the 5 of their papers we have counts for
5 papers
Instella-T2I: Pushing the Limits of 1D Discrete Latent Space Image Generation
Ze Wang, Hao Chen, Benran Hu +7
Image tokenization plays a critical role in reducing the computational demands of modeling high-resolution images, significantly improving the efficiency of image and multimodal un…
Improving the Diffusability of Autoencoders
Ivan Skorokhodov, Sharath Girish, Benran Hu +5
Latent diffusion models have emerged as the leading approach for generating high-quality images and videos, utilizing compressed latent representations to reduce the computational…
SANeRF-HQ: Segment Anything for NeRF in High Quality
Yichen Liu, Benran Hu, Chi-Keung Tang +1
Recently, the Segment Anything Model (SAM) has showcased remarkable capabilities of zero-shot segmentation, while NeRF (Neural Radiance Fields) has gained popularity as a method fo…
Instance Neural Radiance Field
Yichen Liu, Benran Hu, Junkai Huang +2
This paper presents one of the first learning-based NeRF 3D instance segmentation pipelines, dubbed as Instance Neural Radiance Field, or Instance NeRF. Taking a NeRF pretrained fr…
NeRF-RPN: A general framework for object detection in NeRFs
Benran Hu, Junkai Huang, Yichen Liu +2
This paper presents the first significant object detection framework, NeRF-RPN, which directly operates on NeRF. Given a pre-trained NeRF model, NeRF-RPN aims to detect all boundin…