9 citations · 32 across the 10 of their papers we have counts for
10 papers
CDI3D: Cross-guided Dense-view Interpolation for 3D Reconstruction
Zhiyuan Wu, Xibin Song, Senbo Wang +8
3D object reconstruction from single-view image is a fundamental task in computer vision with wide-ranging applications. Recent advancements in Large Reconstruction Models (LRMs) h…
Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and Texture Generation
Jiayu Yang, Taizhang Shang, Weixuan Sun +7
This report presents a comprehensive framework for generating high-quality 3D shapes and textures from diverse input prompts, including single images, multi-view images, and text d…
MARS: Mesh AutoRegressive Model for 3D Shape Detailization
Jingnan Gao, Weizhe Liu, Weixuan Sun +8
State-of-the-art methods for mesh detailization predominantly utilize Generative Adversarial Networks (GANs) to generate detailed meshes from coarse ones. These methods typically l…
BAG: Body-Aligned 3D Wearable Asset Generation
Zhongjin Luo, Yang Li, Mingrui Zhang +8
While recent advancements have shown remarkable progress in general 3D shape generation models, the challenge of leveraging these approaches to automatically generate wearable 3D a…
LAM3D: Large Image-Point-Cloud Alignment Model for 3D Reconstruction from Single Image
Ruikai Cui, Xibin Song, Weixuan Sun +8
Large Reconstruction Models have made significant strides in the realm of automated 3D content generation from single or multiple input images. Despite their success, these models…
NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generation
Ruikai Cui, Weizhe Liu, Weixuan Sun +9
3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints. Existing methods often decompose 3D shapes into a sequence of localized c…
Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane
Han Yan, Yang Li, Zhennan Wu +9
We present Frankenstein, a diffusion-based framework that can generate semantic-compositional 3D scenes in a single pass. Unlike existing methods that output a single, unified 3D s…
BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation
Zhennan Wu, Yang Li, Han Yan +8
We present BlockFusion, a diffusion-based model that generates 3D scenes as unit blocks and seamlessly incorporates new blocks to extend the scene. BlockFusion is trained using dat…
Perceptual Extreme Super Resolution Network with Receptive Field Block
Taizhang Shang, Qiuju Dai, Shengchen Zhu +2
Perceptual Extreme Super-Resolution for single image is extremely difficult, because the texture details of different images vary greatly. To tackle this difficulty, we develop a s…
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
Kai Zhang, Shuhang Gu, Radu Timofte +60
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input im…