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20232026
most citedLLaMA-Mesh: Unifying 3D Mesh Generation with Language Models

3 citations · 11 across the 10 of their papers we have counts for

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8 papers · 1 filter

cs.LG2024★ 3 cited

LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models

Zhengyi Wang, Jonathan Lorraine, Yikai Wang +4

This work explores expanding the capabilities of large language models (LLMs) pretrained on text to generate 3D meshes within a unified model. This offers key advantages of (1) lev…

cs.CV2024

MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization

Yiwen Chen, Yikai Wang, Yihao Luo +5

Meshes are the de facto 3D representation in the industry but are labor-intensive to produce. Recently, a line of research has focused on autoregressively generating meshes. This a…

cs.CV2024

Freeplane: Unlocking Free Lunch in Triplane-Based Sparse-View Reconstruction Models

Wenqiang Sun, Zhengyi Wang, Shuo Chen +4

Creating 3D assets from single-view images is a complex task that demands a deep understanding of the world. Recently, feed-forward 3D generative models have made significant progr…

cs.CV2024★ 1 cited

PivotMesh: Generic 3D Mesh Generation via Pivot Vertices Guidance

Haohan Weng, Yikai Wang, Tong Zhang +2

Generating compact and sharply detailed 3D meshes poses a significant challenge for current 3D generative models. Different from extracting dense meshes from neural representation,…

cs.CV2024★ 1 cited

Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels

Yikai Wang, Xinzhou Wang, Zilong Chen +3

Video generative models are receiving particular attention given their ability to generate realistic and imaginative frames. Besides, these models are also observed to exhibit stro…

cs.CV2024★ 2 cited

FlexiDreamer: Single Image-to-3D Generation with FlexiCubes

Ruowen Zhao, Zhengyi Wang, Yikai Wang +2

3D content generation has wide applications in various fields. One of its dominant paradigms is by sparse-view reconstruction using multi-view images generated by diffusion models.…

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

CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model

Zhengyi Wang, Yikai Wang, Yifei Chen +6

Feed-forward 3D generative models like the Large Reconstruction Model (LRM) have demonstrated exceptional generation speed. However, the transformer-based methods do not leverage t…