  Previous efforts have managed to generate production-ready 3D assets from
text or images. However, these methods primarily employ NeRF or 3D Gaussian
representations, which are not adept at producing smooth, high-quality
geometries required by modern rendering pipelines. In this paper, we propose
LDM, a novel feed-forward framework capable of generating high-fidelity,
illumination-decoupled textured mesh from a single image or text prompts. We
firstly utilize a multi-view diffusion model to generate sparse multi-view
inputs from single images or text prompts, and then a transformer-based model
is trained to predict a tensorial SDF field from these sparse multi-view image
inputs. Finally, we employ a gradient-based mesh optimization layer to refine
this model, enabling it to produce an SDF field from which high-quality
textured meshes can be extracted. Extensive experiments demonstrate that our
method can generate diverse, high-quality 3D mesh assets with corresponding
decomposed RGB textures within seconds.
