1. https://huggingface.co/spaces/Yuanshi/OminiControl
2.
https://huggingface.co/spaces/Qwen/QwQ-32B-preview
GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation
GaussianAnything (arXiv 2024) [code, project page] is a native 3D diffusion model that supports high-quality 2D Gaussians generation. It first trains a 3D VAE on Objaverse, which compress each 3D asset into a compact point cloud-structured latent. After that, a image/text-conditioned diffusion model is trained following LDM paradigm. The model used in the demo adopts 3D DiT architecture and flow-matching framework, and supports single-image condition. It is trained on 8 A100 GPUs for 1M iterations with batch size 256. Locally, on an NVIDIA A100/A10 GPU, each image-conditioned diffusion generation can be done within 20 seconds (time varies due to the adaptive-step ODE solver used in flow-mathcing.) Upload an image of an object or click on one of the provided examples to see how the GaussianAnything works.
The 3D viewer will render a .glb point cloud exported from the centers of the surfel Gaussians, and an integrated TSDF mesh. Besides, you can find the intermediate stage-1 point cloud in the Tab (Stage-1 Output).
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