5 papers
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…
MotionPersona: Characteristics-aware Locomotion Control
Mingyi Shi, Wei Liu, Jidong Mei +4
We present MotionPersona, a novel real-time character controller that allows users to characterize a character by specifying attributes such as physical traits, mental states, and…
CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner
Weiyu Li, Jiarui Liu, Hongyu Yan +5
We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed…
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,…