Search Results for "3d shapes"

TRELLIS.2 is a cutting-edge open-source model and codebase for high-fidelity 3D asset generation from 2D images, developed to push forward the state of the art in image-to-3D generation. At its core is a novel sparse voxel structure called O-Voxel that jointly encodes both geometry and surface appearance, enabling reconstruction and generation of complex 3D shapes with arbitrary topology, open surfaces, and physically based rendering (PBR) textures.

Hunyuan3D-1 is an earlier version in the same 3D generation line (the unified framework for text-to-3D and image-to-3D tasks) by Tencent Hunyuan. It provides a framework combining shape generation and texture synthesis, enabling users to create 3D assets from images or text conditions. While less advanced than version 2.1, it laid the foundations for the later PBR, higher resolution, and open-source enhancements. (Note: less detailed public documentation was found for Hunyuan3D-1 compared to...

Hunyuan3D-2.1 is Tencent Hunyuan’s advanced 3D asset generation system that produces high-fidelity 3D models with Physically Based Rendering (PBR) textures. It is fully open-source with released model weights, training, and inference code. It improves on prior versions by using a PBR texture pipeline (enabling realistic material effects like reflections and subsurface scattering) and allowing community fine-tuning and extension. It supports both shape generation (mesh geometry) and texture...

MeshCNN is a deep learning framework designed specifically for processing 3D triangular mesh data using convolutional neural networks. Unlike traditional CNNs that operate on images or voxel grids, MeshCNN performs convolution operations directly on the edges of mesh structures. This design allows the model to capture geometric relationships between mesh elements while preserving the underlying topology of 3D shapes.

DeepSDF is a deep learning framework for continuous 3D shape representation using Signed Distance Functions (SDFs), as presented in the CVPR 2019 paper DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation by Park et al. The framework learns a continuous implicit function that maps 3D coordinates to their corresponding signed distances from object surfaces, allowing compact, high-fidelity shape modeling.