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20242026
most citedLiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.GR2025

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…

cs.CV20241 cited

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…