1 citations · 2 across the 10 of their papers we have counts for
12 papers
Gate3DGen: A Pre-processing Model that Selectively Purifies Real-World Images for 3D Generation
Xiaolin Li, Zihao Huang, Fanpeng Meng +5
Single-image 3D generation has achieved high fidelity on curated benchmarks, yet degrades sharply on real-world images with uneven illumination, blur, and occlusions. Purifying suc…
SCULPT: Subtractive Composition for 3D Part Generation
Sikuang Li, Chen Yang, Jiemin Fang +5
Part-aware 3D generation aims to create digital assets that are coherent as complete objects while exposing structural parts for editing, material assignment, animation, and reuse.…
ProxyUp: Training-Free Proxy-Conditioned Video Generation for Controllable Dynamics
Zanwei Zhou, Jiazhong Cen, Jiemin Fang +7
Precise control over complex dynamics remains challenging for modern video generative models, as text prompts alone often cannot specify physically plausible, fine-grained motion a…
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…
Efficient and Robust Video Defense Framework against 3D-field Personalized Talking Face
Rui-qing Sun, Xingshan Yao, Tian Lan +6
State-of-the-art 3D-field video-referenced Talking Face Generation (TFG) methods synthesize high-fidelity personalized talking-face videos in real time by modeling 3D geometry and…
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…
LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors
Yabo Chen, Chen Yang, Jiemin Fang +6
Single-image 3D reconstruction remains a fundamental challenge in computer vision due to inherent geometric ambiguities and limited viewpoint information. Recent advances in Latent…
Topology-aware Human Avatars with Semantically-guided Gaussian Splatting
Haoyu Zhao, Chen Yang, Hao Wang +2
Reconstructing photo-realistic and topology-aware animatable human avatars from monocular videos remains challenging in computer vision and graphics. Recently, methods using 3D Gau…
3D-Consistent Human Avatars with Sparse Inputs via Gaussian Splatting and Contrastive Learning
Haoyu Zhao, Hao Wang, Chen Yang +1
Existing approaches for human avatar generation--both NeRF-based and 3D Gaussian Splatting (3DGS) based--struggle with maintaining 3D consistency and exhibit degraded detail recons…