TRELLIS.2

Headlines · 2

1. 5d
   WorldRecon: Generative multi-view mesh reconstruction for large-scale 3D scenes

   A new arXiv paper introduces WorldRecon, a generative multi-view reconstruction framework that recovers complete, large-scale 3D scene meshes from unposed RGB images. It uses a coarse, noisy global point cloud and camera poses estimated from the inputs as a global scaffold, partitions the scene into overlapping spatial blocks, and equips the native 3D generative model TRELLIS.2 with reconstruction-based conditioning to produce sparse structure, detailed geometry and PBR materials from each block's local point-cloud crop and relevant image observations. To keep scenes coherent, the authors add neighbour-aware latent inpainting and a causal world-generation procedure that propagates structural, geometric and material context across blocks. The paper reports state-of-the-art results, reducing Chamfer distance by 14.9% and 43.1% and improving F-score by 17.3% and 12.0% over the strongest baselines on synthetic and real-world indoor scenes respectively, with strong claimed sim-to-real generalization.

   Hugging Face · Papers · 🔥 0
2. 6d
   Microsoft open-sources TRELLIS.2, a 4B image-to-3D model with PBR output

   Microsoft has published TRELLIS.2 on GitHub, a 4-billion-parameter image-to-3D generative model built on a "field-free" sparse voxel representation called O-Voxel, which the repo says handles open surfaces, non-manifold geometry and internal enclosed structures and predicts PBR surface attributes such as base color, roughness, metallic and opacity. The project reports generation times on an NVIDIA H100 of about 3 seconds at 512³ (2s shape + 1s material), roughly 17 seconds at 1024³ and about 60 seconds at 1536³, plus under 10 seconds on a single CPU to convert a textured mesh to O-Voxel and under 100ms on CUDA for the reverse. Code, data tooling and training scripts are provided and the TRELLIS.2-4B weights are on Hugging Face, but the code is Linux-only and needs an NVIDIA GPU with at least 24GB of memory (verified on A100 and H100, CUDA 12.4).

   GitHub Trending · Python · 🔥 0

Experience and discussion from the community

Share my TRELLIS.2 experienceAsk about TRELLIS.2