  We present FashionEngine, an interactive 3D human generation and editing
system that creates 3D digital humans via user-friendly multimodal controls
such as natural languages, visual perceptions, and hand-drawing sketches.
FashionEngine automates the 3D human production with three key components: 1) A
pre-trained 3D human diffusion model that learns to model 3D humans in a
semantic UV latent space from 2D image training data, which provides strong
priors for diverse generation and editing tasks. 2) Multimodality-UV Space
encoding the texture appearance, shape topology, and textual semantics of human
clothing in a canonical UV-aligned space, which faithfully aligns the user
multimodal inputs with the implicit UV latent space for controllable 3D human
editing. The multimodality-UV space is shared across different user inputs,
such as texts, images, and sketches, which enables various joint multimodal
editing tasks. 3) Multimodality-UV Aligned Sampler learns to sample
high-quality and diverse 3D humans from the diffusion prior. Extensive
experiments validate FashionEngine's state-of-the-art performance for
conditional generation/editing tasks. In addition, we present an interactive
user interface for our FashionEngine that enables both conditional and
unconditional generation tasks, and editing tasks including pose/view/shape
control, text-, image-, and sketch-driven 3D human editing and 3D virtual
try-on, in a unified framework. Our project page is at:
https://taohuumd.github.io/projects/FashionEngine.
