TeCH: Text-guided Reconstruction of Lifelike Clothed Humans
Authors: name: Yangyi Huang, name: Hongwei Yi, name: Yuliang Xiu, name: Tingting Liao, name: Jiaxiang Tang, name: Deng Cai, name: Justus Thies

Despite recent research advancements in reconstructing clothed humans from a
single image, accurately restoring the "unseen regions" with high-level details
remains an unsolved challenge that lacks attention. Existing methods often
generate overly smooth back-side surfaces with a blurry texture. But how to
effectively capture all visual attributes of an individual from a single image,
which are sufficient to reconstruct unseen areas (e.g., the back view)?
Motivated by the power of foundation models, TeCH reconstructs the 3D human by
leveraging 1) descriptive text prompts (e.g., garments, colors, hairstyles)
which are automatically generated via a garment parsing model and Visual
Question Answering (VQA), 2) a personalized fine-tuned Text-to-Image diffusion
model (T2I) which learns the "indescribable" appearance. To represent
high-resolution 3D clothed humans at an affordable cost, we propose a hybrid 3D
representation based on DMTet, which consists of an explicit body shape grid
and an implicit distance field. Guided by the descriptive prompts +
personalized T2I diffusion model, the geometry and texture of the 3D humans are
optimized through multi-view Score Distillation Sampling (SDS) and
reconstruction losses based on the original observation. TeCH produces
high-fidelity 3D clothed humans with consistent & delicate texture, and
detailed full-body geometry. Quantitative and qualitative experiments
demonstrate that TeCH outperforms the state-of-the-art methods in terms of
reconstruction accuracy and rendering quality. The code will be publicly
available for research purposes at https://huangyangyi.github.io/tech