Papers for

3d content creators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Multi-view method improves 3d shape generation with guided noise control

ReconPlusGen: Injecting Reconstruction Prior into Multi-view 3D Generation through Noise Inversion and Modulation

Abstract: Qualitative results and an illustration of our core idea. Top left: reconstruction results on benchmark images. Top right: reconstruction results on real-world images. Bottom: illustration of reconstruction-guided noise initialization and modulation. Given multiple input images, we predict a point cloud in canonical space, deterministically inject the predicted geometry into the diffusion process through noise inversion, and modulate the resulting noise to preserve the generative flexibility required to complete unobserved regions and refine visible geometry.

Thu 10 SeptComputer Vision and Pattern Recognition

The gist

Creating 3D shapes from multiple pictures is a tricky job because the computer has to guess parts it can't see. The authors present a way to guide this process by predicting a rough 3D shape first and then mixing that knowledge carefully into a generative method that normally produces random results. This helps the computer keep the important parts from the pictures while still being creative to fill in missing details. Their method lets the 3D shapes look more accurate and realistic.

Open → 2609.11129v1