1 citations · 2 across the 8 of their papers we have counts for
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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…
Tackling View-Dependent Semantics in 3D Language Gaussian Splatting
Jiazhong Cen, Xudong Zhou, Jiemin Fang +5
Recent advancements in 3D Gaussian Splatting (3D-GS) enable high-quality 3D scene reconstruction from RGB images. Many studies extend this paradigm for language-driven open-vocabul…
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…