280 citations · 328 across the 12 of their papers we have counts for
13 papers
Text-Image Conditioned 3D Generation
Jiazhong Cen, Jiemin Fang, Sikuang Li +8
High-quality 3D assets are essential for VR/AR, industrial design, and entertainment, motivating growing interest in generative models that create 3D content from user prompts. Mos…
WorldGrow: Generating Infinite 3D World
Sikuang Li, Chen Yang, Jiemin Fang +6
We tackle the challenge of generating the infinitely extendable 3D world -- large, continuous environments with coherent geometry and realistic appearance. Existing methods face ke…
UniLat3D: Geometry-Appearance Unified Latents for Single-Stage 3D Generation
Guanjun Wu, Jiemin Fang, Chen Yang +11
High-fidelity 3D asset generation is crucial for various industries. While recent 3D pretrained models show strong capability in producing realistic content, most are built upon di…
Few-step Flow for 3D Generation via Marginal-Data Transport Distillation
Zanwei Zhou, Taoran Yi, Jiemin Fang +5
Flow-based 3D generation models typically require dozens of sampling steps during inference. Though few-step distillation methods, particularly Consistency Models (CMs), have achie…
Snap-Snap: Taking Two Images to Reconstruct 3D Human Gaussians in Milliseconds
Jia Lu, Taoran Yi, Jiemin Fang +6
Reconstructing 3D human bodies from sparse views has been an appealing topic, which is crucial to broader the related applications. In this paper, we propose a quite challenging bu…
Segment Any 4D Gaussians
Shengxiang Ji, Guanjun Wu, Jiemin Fang +5
Modeling, understanding, and reconstructing the real world are crucial in XR/VR. Recently, 3D Gaussian Splatting (3D-GS) methods have shown remarkable success in modeling and under…
GaussianDreamerPro: Text to Manipulable 3D Gaussians with Highly Enhanced Quality
Taoran Yi, Jiemin Fang, Zanwei Zhou +7
Recently, 3D Gaussian splatting (3D-GS) has achieved great success in reconstructing and rendering real-world scenes. To transfer the high rendering quality to generation tasks, a…
Fast High Dynamic Range Radiance Fields for Dynamic Scenes
Guanjun Wu, Taoran Yi, Jiemin Fang +2
Neural Radiances Fields (NeRF) and their extensions have shown great success in representing 3D scenes and synthesizing novel-view images. However, most NeRF methods take in low-dy…
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…
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.…
Generalizable Neural Voxels for Fast Human Radiance Fields
Taoran Yi, Jiemin Fang, Xinggang Wang +1
Rendering moving human bodies at free viewpoints only from a monocular video is quite a challenging problem. The information is too sparse to model complicated human body structure…
Fast Dynamic Radiance Fields with Time-Aware Neural Voxels
Jiemin Fang, Taoran Yi, Xinggang Wang +5
Neural radiance fields (NeRF) have shown great success in modeling 3D scenes and synthesizing novel-view images. However, most previous NeRF methods take much time to optimize one…