mesh-generation

MeshWeaver presents an autoregressive mesh generation framework that directly predicts vertices using a multi-level sparse-voxel encoder, achieving state-of-the-art compression and geometric fidelity for high-poly meshes.

A training-free 4D mesh generation approach using Spatio-Temporal Attention Chains accelerates creation to 9 seconds (13x speedup) while improving temporal consistency and scaling to longer sequences, with zero-shot capabilities for tracking and camera estimation.

This paper introduces a triangulation-agnostic flow matching method for mesh-based signal generation, using Matérn processes as noise and PoissonNet as denoiser, achieving high-quality results on large meshes.

A developer ported Microsoft's TRELLIS.2 image-to-3D model to run on Apple Silicon Macs by replacing CUDA-only dependencies with PyTorch MPS equivalents, enabling offline 3D mesh generation without requiring NVIDIA GPUs.

A community port of Microsoft's TRELLIS.2 image-to-3D generation model to run natively on Apple Silicon Macs via PyTorch MPS, eliminating the need for NVIDIA GPUs and generating high-quality 3D meshes with 400K+ vertices in ~3.5 minutes on M4 Pro.