VAST-AI

TripoSG - High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models TripoSG is a state-of-the-art image-to-3D generation foundation model that leverages large-scale rectified flow transformers to produce high-fidelity 3D shapes from single images. TripoSG utilizes a novel architecture combining: - Rectified Flow (RF) based Transformer for stable, linear trajectory modeling - Advanced VAE with SDF-based representation and hybrid geometric supervision - Cross-attention mechanism for image feature condition - 1.5B parameters operating on 2048 latent tokens This model is designed for: - Converting single images to high-quality 3D meshes - Creative and design applications - Gaming and VFX asset creation - Prototyping and visualization For detailed usage instructions, please visit our GitHub repository. TripoSG is developed by Tripo, VAST AI Research, pushing the boundaries of 3D Generative AI. For more information: - GitHub Repository - Paper - Gradio Demo

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Paper: One Model to Rig Them All: Diverse Skeleton Rigging with UniRig Code: UniRig Project Page: https://zjp-shadow.github.io/works/UniRig/ 🚨 Note: This model card currently contains only the Skeleton&Skinning Prediction component of the UniRig framework, trained specifically on the Articulation-XL2.0 dataset. The skinning weight prediction model and models trained on the Rig-XL/VRoid datasets described in the paper will be released separately at a later date. UniRig is a unified framework for automatic skeletal rigging of 3D models, developed by Tsinghua University and by Tripo (VAST AI Research). It addresses the significant bottleneck of rigging in 3D animation pipelines by providing a powerful model capable of generating high-quality skeleton hierarchies and skinning weights for a diverse range of input meshes, including humans, animals, fictional characters, and even inorganic structures. This release provides the autoregressive skeleton prediction model from the UniRig framework. Its purpose is to automatically generate a topologically valid skeleton hierarchy for a given 3D input mesh. The model leverages: 1. A Shape Encoder: Processes the input mesh (as a point cloud) to capture geometric features. 2. An OPT-based Transformer: Autoregressively predicts a sequence of tokens representing the skeleton structure. 3. Skeleton Tree Tokenization: A novel method (as described in the UniRig paper) to efficiently encode the skeleton's hierarchical structure and joint coordinates into a sequence suitable for the transformer. This model serves as the first stage of the full UniRig pipeline. The predicted skeleton can be used as input for the forthcoming skinning weight prediction model or other downstream rigging tasks. This Hugging Face model release includes: ✅ Skeleton Prediction Model Checkpoint: Trained on the Articulation-XL2.0 dataset. ✅ Coming Soon: Skinning Weight Prediction Model. ✅ Coming Soon: Rig-XL and VRoid datasets. ⏳ Coming Soon: Model checkpoints trained on Rig-XL and VRoid datasets (representing the full results reported in the paper). Follow VAST AI Research updates for future releases. For detailed usage instructions, please visit our GitHub repository.