2 citations · 4 across the 12 of their papers we have counts for
13 papers
AssetGen: Deployable 3D Asset Generation at Interactive Speed
Dilin Wang, Xiaoyu Xiang, Kihyuk Sohn +14
While 3D generation is progressing rapidly, recent work has often focused on obtaining high-resolution assets, leaving user experience and deployability as afterthoughts. We presen…
Co-generation of Layout and Shape from Text via Autoregressive 3D Diffusion
Zhenggang Tang, Yuehao Wang, Yuchen Fan +9
Recent text-to-scene generation approaches largely reduced the manual efforts required to create 3D scenes. However, their focus is either to generate a scene layout or to generate…
Shoot-Bounce-3D: Single-Shot Occlusion-Aware 3D from Lidar by Decomposing Two-Bounce Light
Tzofi Klinghoffer, Siddharth Somasundaram, Xiaoyu Xiang +5
3D scene reconstruction from a single measurement is challenging, especially in the presence of occluded regions and specular materials, such as mirrors. We address these challenge…
WorldGen: From Text to Traversable and Interactive 3D Worlds
Dilin Wang, Hyunyoung Jung, Tom Monnier +22
We introduce WorldGen, a system that enables the automatic creation of large-scale, interactive 3D worlds directly from text prompts. Our approach transforms natural language descr…
Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds
Xiaoyu Xiang, Liat Sless Gorelik, Yuchen Fan +5
We present Make-A-Texture, a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively genera…
MV-DUSt3R+: Single-Stage Scene Reconstruction from Sparse Views In 2 Seconds
Zhenggang Tang, Yuchen Fan, Dilin Wang +4
Recent sparse multi-view scene reconstruction advances like DUSt3R and MASt3R no longer require camera calibration and camera pose estimation. However, they only process a pair of…
3D Mesh Editing using Masked LRMs
Will Gao, Dilin Wang, Yuchen Fan +6
We present a novel approach to shape editing, building on recent progress in 3D reconstruction from multi-view images. We formulate shape editing as a conditional reconstruction pr…
WaSt-3D: Wasserstein-2 Distance for Scene-to-Scene Stylization on 3D Gaussians
Dmytro Kotovenko, Olga Grebenkova, Nikolaos Sarafianos +8
While style transfer techniques have been well-developed for 2D image stylization, the extension of these methods to 3D scenes remains relatively unexplored. Existing approaches de…
MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction
Shitao Tang, Jiacheng Chen, Dilin Wang +6
This paper presents a neural architecture MVDiffusion++ for 3D object reconstruction that synthesizes dense and high-resolution views of an object given one or a few images without…
PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar
Tzofi Klinghoffer, Xiaoyu Xiang, Siddharth Somasundaram +4
3D reconstruction from a single-view is challenging because of the ambiguity from monocular cues and lack of information about occluded regions. Neural radiance fields (NeRF), whil…
Training Neural Networks on RAW and HDR Images for Restoration Tasks
Andrew Yanzhe Ke, Lei Luo, Xiaoyu Xiang +4
The vast majority of standard image and video content available online is represented in display-encoded color spaces, in which pixel values are conveniently scaled to a limited ra…
Customizing 360-Degree Panoramas through Text-to-Image Diffusion Models
Hai Wang, Xiaoyu Xiang, Yuchen Fan +1
Personalized text-to-image (T2I) synthesis based on diffusion models has attracted significant attention in recent research. However, existing methods primarily concentrate on cust…
MoVideo: Motion-Aware Video Generation with Diffusion Models
Jingyun Liang, Yuchen Fan, Kai Zhang +3
While recent years have witnessed great progress on using diffusion models for video generation, most of them are simple extensions of image generation frameworks, which fail to ex…