AI Technology & Industry Review
A research team innovative single-stage category-agnostic diffusion model. This model can generate 3D Neural Radiance Fields (NeRFs) from either text or a single-image input condition through direct model inference, enabling the creation of diverse high-fidelity 3D objects in just 30s/asset.
In the new paper Instruct-NeRF2NeRF: Editing 3D Scenes With Instructions, a UC Berkeley research team presents Instruct-NeRF2NeRF, an approach for editing 3D NeRF scenes through natural language text instructions. The proposed method can edit large-scale, real-world 3D scenes with improved ease of use and realism.
In the new paper RealFusion: 360° Reconstruction of Any Object from a Single Image, an Oxford University research team leverages a diffusion model to generate 360° reconstructions of objects from a single image. Their RealFusion approach achieves state-of-the-art performance on monocular 3D reconstruction benchmarks.
Researchers from the University of Washington, Virginia Tech and Facebook have introduced an algorithm that can reconstruct dense, geometrically consistent depth for all pixels in monocular videos.
A team of researchers from NVIDIA and Heidelberg University recently introduced an open-source self-supervised learning technique for viewpoint estimation of general objects that draws on such freely available Internet images.