1 citations · 3 across the 7 of their papers we have counts for
7 papers
DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation
Haoran Li, Yuli Tian, Kun Lan +4
Generating 3D scenes from natural language holds great promise for applications in gaming, film, and design. However, existing methods struggle with automation, 3D consistency, and…
Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neuroscience
Zinan Liu, Haoran Li, Jingyi Lu +6
Autonomous AI is no longer a hard-to-reach concept, it enables the agents to move beyond executing tasks to independently addressing complex problems, adapting to change while hand…
Text-to-3D Generation by 2D Editing
Haoran Li, Yuli Tian, Yonghui Wang +4
Distilling 3D representations from pretrained 2D diffusion models is essential for 3D creative applications across gaming, film, and interior design. Current SDS-based methods are…
DreamScene: 3D Gaussian-based Text-to-3D Scene Generation via Formation Pattern Sampling
Haoran Li, Haolin Shi, Wenli Zhang +5
Text-to-3D scene generation holds immense potential for the gaming, film, and architecture sectors. Despite significant progress, existing methods struggle with maintaining high qu…
Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise
Qinglong Huang, Haoran Li, Yong Liao +2
Neural Radiance Field (NeRF) has been proposed as an innovative advancement in 3D reconstruction techniques. However, little research has been conducted on the issues of informatio…
2D-Guided 3D Gaussian Segmentation
Kun Lan, Haoran Li, Haolin Shi +4
Recently, 3D Gaussian, as an explicit 3D representation method, has demonstrated strong competitiveness over NeRF (Neural Radiance Fields) in terms of expressing complex scenes and…
3D-GOI: 3D GAN Omni-Inversion for Multifaceted and Multi-object Editing
Haoran Li, Long Ma, Haolin Shi +4
The current GAN inversion methods typically can only edit the appearance and shape of a single object and background while overlooking spatial information. In this work, we propose…