8 citations · 21 across the 6 of their papers we have counts for
6 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…
Gaussian Shell Maps for Efficient 3D Human Generation
Rameen Abdal, Wang Yifan, Zifan Shi +6
Efficient generation of 3D digital humans is important in several industries, including virtual reality, social media, and cinematic production. 3D generative adversarial networks…
State of the Art on Diffusion Models for Visual Computing
Ryan Po, Wang Yifan, Vladislav Golyanik +15
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, edi…
Efficient 3D Articulated Human Generation with Layered Surface Volumes
Yinghao Xu, Wang Yifan, Alexander W. Bergman +3
Access to high-quality and diverse 3D articulated digital human assets is crucial in various applications, ranging from virtual reality to social platforms. Generative approaches,…
Articulated 3D Head Avatar Generation using Text-to-Image Diffusion Models
Alexander W. Bergman, Wang Yifan, Gordon Wetzstein
The ability to generate diverse 3D articulated head avatars is vital to a plethora of applications, including augmented reality, cinematography, and education. Recent work on text-…
DehazeNeRF: Multiple Image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields
Wei-Ting Chen, Wang Yifan, Sy-Yen Kuo +1
Neural radiance fields (NeRFs) have demonstrated state-of-the-art performance for 3D computer vision tasks, including novel view synthesis and 3D shape reconstruction. However, the…