19 citations · 25 across the 8 of their papers we have counts for
5 papers · 1 filter
SpatialCrafter: Single Image World Modeling with Generative 3D Proxies
Chuan Fang, Lingteng Qiu, Yixun Liang +8
Explorable image-to-scene generation is essential for applications in gaming, robotics, and virtual reality. Existing methods based on video diffusion model (VDM) commonly rely on…
TopoMesh: High-Fidelity Mesh Autoencoding via Topological Unification
Guan Luo, Xiu Li, Rui Chen +6
The dominant paradigm for high-fidelity 3D generation relies on a VAE-Diffusion pipeline, where the VAE's reconstruction capability sets a firm upper bound on generation quality. A…
UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes
Yixun Liang, Kunming Luo, Xiao Chen +5
We present UniTEX, a novel two-stage 3D texture generation framework to create high-quality, consistent textures for 3D assets. Existing approaches predominantly rely on UV-based i…
Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders
Rui Chen, Jianfeng Zhang, Yixun Liang +7
Recent 3D content generation pipelines commonly employ Variational Autoencoders (VAEs) to encode shapes into compact latent representations for diffusion-based generation. However,…
SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3D
Weiyu Li, Rui Chen, Xuelin Chen +1
It is inherently ambiguous to lift 2D results from pre-trained diffusion models to a 3D world for text-to-3D generation. 2D diffusion models solely learn view-agnostic priors and t…