9 citations · 15 across the 11 of their papers we have counts for
12 papers
Beyond Global Latents: Chunk-Based Sparse Grid VAE for Scalable 3D Modeling
Kaiyi Zhang, Zhihao Liang, Haolin Liu +8
Sparse voxel grids preserve the spatial structure needed for detailed 3D reconstruction, but their memory still grows rapidly with resolution as active surface cells increase. We i…
MD-Face: MoE-Enhanced Label-Free Disentangled Representation for Interactive Facial Attribute Editing
Xuan Cui, Yunfei Zhao, Bo Liu +2
GAN-based facial attribute editing is widely used in virtual avatars and social media but often suffers from attribute entanglement, where modifying one face attribute unintentiona…
ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
Jovana Kondic, Pengyuan Li, Dhiraj Joshi +24
Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language…
HY3D-Bench: Generation of 3D Assets
Team Hunyuan3D, :, Bowen Zhang +22
While recent advances in neural representations and generative models have revolutionized 3D content creation, the field remains constrained by significant data processing bottlene…
LATTICE: Democratize High-Fidelity 3D Generation at Scale
Zeqiang Lai, Yunfei Zhao, Zibo Zhao +5
We present LATTICE, a new framework for high-fidelity 3D asset generation that bridges the quality and scalability gap between 3D and 2D generative models. While 2D image synthesis…
NaTex: Seamless Texture Generation as Latent Color Diffusion
Zeqiang Lai, Yunfei Zhao, Zibo Zhao +5
We present NaTex, a native texture generation framework that predicts texture color directly in 3D space. In contrast to previous approaches that rely on baking 2D multi-view image…
Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets
Team Hunyuan3D, :, Bowen Zhang +17
Recent advances in 3D-native generative models have accelerated asset creation for games, film, and design. However, most methods still rely primarily on image or text conditioning…
X-Part: high fidelity and structure coherent shape decomposition
Xinhao Yan, Jiachen Xu, Yang Li +8
Generating 3D shapes at part level is pivotal for downstream applications such as mesh retopology, UV mapping, and 3D printing. However, existing part-based generation methods ofte…
Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details
Zeqiang Lai, Yunfei Zhao, Haolin Liu +23
In this report, we present Hunyuan3D 2.5, a robust suite of 3D diffusion models aimed at generating high-fidelity and detailed textured 3D assets. Hunyuan3D 2.5 follows two-stages…
Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material
Team Hunyuan3D, Shuhui Yang, Mingxin Yang +50
3D AI-generated content (AIGC) is a passionate field that has significantly accelerated the creation of 3D models in gaming, film, and design. Despite the development of several gr…
Unleashing Vecset Diffusion Model for Fast Shape Generation
Zeqiang Lai, Yunfei Zhao, Zibo Zhao +10
3D shape generation has greatly flourished through the development of so-called "native" 3D diffusion, particularly through the Vecset Diffusion Model (VDM). While recent advanceme…
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
Zibo Zhao, Zeqiang Lai, Qingxiang Lin +71
We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-sca…