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20232026
most citedUnique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image

7 citations · 20 across the 23 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.CV2024

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion

Wenqiang Sun, Shuo Chen, Fangfu Liu +4

In this paper, we introduce \textbf{DimensionX}, a framework designed to generate photorealistic 3D and 4D scenes from just a single image with video diffusion. Our approach begins…

cs.CV2024★ 2 cited

ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model

Fangfu Liu, Wenqiang Sun, Hanyang Wang +5

Advancements in 3D scene reconstruction have transformed 2D images from the real world into 3D models, producing realistic 3D results from hundreds of input photos. Despite great s…

cs.CV2024

DreamCinema: Cinematic Transfer with Free Camera and 3D Character

Weiliang Chen, Fangfu Liu, Diankun Wu +3

We are living in a flourishing era of digital media, where everyone has the potential to become a personal filmmaker. Current research on video generation suggests a promising aven…

cs.CV2024★ 1 cited

Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion

Fangfu Liu, Hanyang Wang, Shunyu Yao +3

In recent years, there has been rapid development in 3D generation models, opening up new possibilities for applications such as simulating the dynamic movements of 3D objects and…

cs.CV2024★ 7 cited

Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image

Kailu Wu, Fangfu Liu, Zhihan Cai +5

In this work, we introduce Unique3D, a novel image-to-3D framework for efficiently generating high-quality 3D meshes from single-view images, featuring state-of-the-art generation…

cs.CV2024

GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds

Shengjun Zhang, Xin Fei, Yueqi Duan

Point clouds captured by different sensors such as RGB-D cameras and LiDAR possess non-negligible domain gaps. Most existing methods design different network architectures and trai…

cs.CV2024

DreamReward: Text-to-3D Generation with Human Preference

Junliang Ye, Fangfu Liu, Qixiu Li +5

3D content creation from text prompts has shown remarkable success recently. However, current text-to-3D methods often generate 3D results that do not align well with human prefere…

cs.CV2024★ 2 cited

Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation

Fangfu Liu, Hanyang Wang, Weiliang Chen +2

Recent years have witnessed the strong power of 3D generation models, which offer a new level of creative flexibility by allowing users to guide the 3D content generation process t…