18 citations · 38 across the 12 of their papers we have counts for
12 papers
GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation
Yinghao Xu, Zifan Shi, Wang Yifan +5
We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from sparse-view images in around 0.1s. GRM is a feed-forward transformer-based model that efficientl…
DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model
Yinghao Xu, Hao Tan, Fujun Luan +8
We propose \textbf{DMV3D}, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion. Our reconstruction model inco…
In-Domain GAN Inversion for Faithful Reconstruction and Editability
Jiapeng Zhu, Yujun Shen, Yinghao Xu +3
Generative Adversarial Networks (GANs) have significantly advanced image synthesis through mapping randomly sampled latent codes to high-fidelity synthesized images. However, apply…
Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis
Jiapeng Zhu, Ceyuan Yang, Kecheng Zheng +3
Due to the difficulty in scaling up, generative adversarial networks (GANs) seem to be falling from grace on the task of text-conditioned image synthesis. Sparsely-activated mixtur…
Learning Modulated Transformation in GANs
Ceyuan Yang, Qihang Zhang, Yinghao Xu +3
The success of style-based generators largely benefits from style modulation, which helps take care of the cross-instance variation within data. However, the instance-wise stochast…
Improving Out-of-Distribution Robustness of Classifiers via Generative Interpolation
Haoyue Bai, Ceyuan Yang, Yinghao Xu +2
Deep neural networks achieve superior performance for learning from independent and identically distributed (i.i.d.) data. However, their performance deteriorates significantly whe…