Trellis Normal V0 1

An advanced 3D synthesis system developed by Tencent, capable of generating high-resolution textured 3D assets from images or text

TRELLIS Image Large is an image-conditioned version of the large-scale 3D generation model TRELLIS, capable of generating 3D content based on input images.

Depth Anything V2 is currently the most powerful monocular depth estimation model, trained on 595,000 synthetically annotated images and over 62 million real unlabeled images, featuring fine details and robustness.

A monocular depth estimation model based on Vision Transformer (ViT), trained on 1.4 million images, suitable for zero-shot depth prediction tasks.

A monocular depth estimation model based on Vision Transformer (ViT), trained on 1.4 million images

VGGT is a feedforward neural network capable of inferring all key 3D attributes from one, several, or hundreds of views of a scene within seconds.

Depth Anything is a depth estimation model based on the DPT architecture and DINOv2 backbone network, trained on approximately 62 million images, achieving state-of-the-art results in both relative and absolute depth estimation tasks.

Depth Anything V2 is currently the most powerful monocular depth estimation model, trained on a large amount of synthetic and real images, providing fine depth details and high robustness.

MASt3R is a ViT-based image-to-3D model capable of matching images with 3D space

Depth Anything is a depth estimation model based on the DPT architecture, utilizing the DINOv2 backbone network. It was trained on approximately 62 million images and excels in both relative and absolute depth estimation tasks.