7 papers
DealMaTe: Multi-Dimensional Material Transfer via Diffusion Transformer
Nisha Huang, Yizhou Lin, Jie Guo +3
Recently, diffusion-based material transfer methods rely on image fine-tuning or complex architectures with auxiliary networks but face challenges such as text dependency, addition…
Seed3D 2.0: Advancing High-Fidelity Simulation-Ready 3D Content Generation
Diandian Gu, Jing Lin, Gaohong Liu +25
We present Seed3D 2.0, an advanced 3D content generation system built on Seed3D 1.0, with substantial improvements across generation fidelity, simulation-ready capabilities, and ap…
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…
Puppeteer: Rig and Animate Your 3D Models
Chaoyue Song, Xiu Li, Fan Yang +6
Modern interactive applications increasingly demand dynamic 3D content, yet the transformation of static 3D models into animated assets constitutes a significant bottleneck in cont…
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,…
MagicArticulate: Make Your 3D Models Articulation-Ready
Chaoyue Song, Jianfeng Zhang, Xiu Li +8
With the explosive growth of 3D content creation, there is an increasing demand for automatically converting static 3D models into articulation-ready versions that support realisti…