30 citations · 64 across the 23 of their papers we have counts for
6 papers · 1 filter
Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Views
Yabo Chen, Jiemin Fang, Yuyang Huang +7
Synthesizing multi-view 3D from one single image is a significant but challenging task. Zero-1-to-3 methods have achieved great success by lifting a 2D latent diffusion model to th…
Segment Any 3D Gaussians
Jiazhong Cen, Jiemin Fang, Chen Yang +4
This paper presents SAGA (Segment Any 3D GAussians), a highly efficient 3D promptable segmentation method based on 3D Gaussian Splatting (3D-GS). Given 2D visual prompts as input,…
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model
Zelin Peng, Zhengqin Xu, Zhilin Zeng +3
Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data. In the compute…
GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions
Junjie Wang, Jiemin Fang, Xiaopeng Zhang +2
Recently, impressive results have been achieved in 3D scene editing with text instructions based on a 2D diffusion model. However, current diffusion models primarily generate image…
GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models
Taoran Yi, Jiemin Fang, Junjie Wang +6
In recent times, the generation of 3D assets from text prompts has shown impressive results. Both 2D and 3D diffusion models can help generate decent 3D objects based on prompts. 3…
4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
Guanjun Wu, Taoran Yi, Jiemin Fang +6
Representing and rendering dynamic scenes has been an important but challenging task. Especially, to accurately model complex motions, high efficiency is usually hard to guarantee.…