1 research outputs found
TM-NET: Deep Generative Networks for Textured Meshes
We introduce TM-NET, a novel deep generative model for synthesizing textured
meshes in a part-aware manner. Once trained, the network can generate novel
textured meshes from scratch or predict textures for a given 3D mesh, without
image guidance. Plausible and diverse textures can be generated for the same
mesh part, while texture compatibility between parts in the same shape is
achieved via conditional generation. Specifically, our method produces texture
maps for individual shape parts, each as a deformable box, leading to a natural
UV map with minimal distortion. The network separately embeds part geometry
(via a PartVAE) and part texture (via a TextureVAE) into their respective
latent spaces, so as to facilitate learning texture probability distributions
conditioned on geometry. We introduce a conditional autoregressive model for
texture generation, which can be conditioned on both part geometry and textures
already generated for other parts to achieve texture compatibility. To produce
high-frequency texture details, our TextureVAE operates in a high-dimensional
latent space via dictionary-based vector quantization. We also exploit
transparencies in the texture as an effective means to model complex shape
structures including topological details. Extensive experiments demonstrate the
plausibility, quality, and diversity of the textures and geometries generated
by our network, while avoiding inconsistency issues that are common to novel
view synthesis methods