Generating nice meshes in AI pipelines is hard. Our #SIGGRAPHAsia2024 paper proposes a new representation which guarantees manifold connectivity, and even supports polygonal meshes -- a big step for downstream editing and simulation. (1/N). SpaceMesh:

RT @zianwang97: 🚀 We just open-sourced Cosmos DiffusionRenderer!. This major upgrade brings significantly improved video de-lighting and re….

RT @HuanLing6: We are excited to share Cosmos-Drive-Dreams 🚀 .A bold new synthetic data generation (SDG) pipeline powered by world foundati….

📢 GEN3C is now open-sourced, with code released under Apache 2.0 and model weights under the NVIDIA Open Model License!. 🚀 Along with it, we're releasing a GUI tool that lets you specify your desired video trajectory in 3D — come play with it and generate your own!. The

FlexiCubes is now under Apache 2.0! 🎉 . We've been excited to see FlexiCubes extracting high-quality meshes across the community in projects like TRELLIS and TripoSF --- now it's available with a more permissive license. Let's keep building. 💙 .👉 Flexicubes is in NVIDIA

RT @_akhaliq: Nvidia just released Cosmos-Transfer1 on Hugging Face. Conditional World Generation with Adaptive Multimodal Control https://….

Want precise control over the camera trajectory in your generated videos? Need to edit or remove objects in the scene? Check out how we leverage 3D in video models to make it happen! 🎉.

RT @jayzhangjiewu: Excited to share our #CVPR2025 paper: Difix3D+. Difix3D+ reimagines 3D reconstruction with single-step diffusion, distil….

RT @_akhaliq: Nvidia just dropped GEN3C. 3D-Informed World-Consistent Video Generation with Precise Camera Control

RT @nmwsharp: We found a way to generate manifold, polygonal meshes from feature vectors at points -- even if the vectors are random, you a….

We’re excited about the possibilities that new representations offer for learning with meshes—there is still much to do!.Come see our talk at SIGGRAPH Asia in Tokyo to learn more! .(9/N)

Our point cloud-to-mesh model can also be applied to mesh repair by casting it as “mesh inpainting,” without fine-tuning.

We further evaluate our model on the ShapeNet dataset. Our method generates sharp and compact polygonal meshes that match the input conditions and are guaranteed to be manifold. (7/N)

Trained on the ABC dataset, our model generates high-quality meshes with vertices and edges that align accurately with sharp features, highlighting the advantage of directly generating meshes as the output representation. (6/N)

We integrate SpaceMesh with a diffusion model to generate meshes conditioned on geometry provided as a point cloud. Given the same input geometry, our model can generate different styles of meshes depending on the distribution it was trained on. (5/N)

An exciting finding from our project is that the recently proposed spacetime distance [Law and Lucas 2023] is highly effective in representing mesh connectivity. Here, we compare it with other commonly used alternatives for representing graph connectivity. (4/N)

The big idea is to embed discrete halfedge connectivity via a continuous vector space. We carefully define a mapping which translates continuous feature vectors per-vertex into mesh connectivity. Now generating connectivity just means generating feature vectors at points! (3/N)

Some prior methods produce meshes with concise tessellations, but their representation is a "triangle soup" which is often nonmanifold, or has unexpected holes. We directly generate a halfedge mesh data structure, which necessarily describes manifold, closed, oriented surfaces.

RT @amsabour: 📢📢 Align Your Steps: Optimizing Sampling Schedules in Diffusion Models. TL;DR: We introduce a method….

RT @jonLorraine9: New #NVIDIA #GTC24 paper 🎊. We generate high-quality 3D assets in only 400ms from text by combining (a) amortized optimiz….