![]() ![]() NeRFs have proven to be excellent at showcasing photorealistic scenes however, up to now, there have only been a small collection of papers such as NeRFShop and InstructNeRF2NeRF that allowed for removing or changing objects while preserving geometric and photometric consistency. InpaintNeRF360 encodes semantics into image space to allow for accurate object appearance editing. The output is then fine tuned to ensure view consistency even across crazy camera angles and viewing perspectives. While this sounds totally plausible, it gets a little trickier when you expand the request to the full shape of the object, so they use a pre trained image inpainter across each of the images that contain a different view of the object. It’s able to do this through leveraging the Segment Anything Model (SAM) to get a sense of what the specific object is. One large benefit of InpaintNeRF-360 is that it’s able to tackle the full 360 of an object that a user wants to edit, rather than say just one side of it. It uses a promptable segmentation model to allow a user to specify a part of the NeRF that they would like to edit and ensures that the NeRF continues to look photorealistic after its been edited by applying depth-space warping and perceptual priors. In a nutshell, InpaintNeRF-360 allows for the removal or editing of objects within a unbounded 360 degree NeRF. Today we get a new example of that with InpaintNeRF-360. ![]() ![]() I was so excited to see more and more papers with similar functionality. Nvidia has published a blog post on the process, while the research paper is available for open access on arXiv.When InstructNeRF2NeRF first came out, I knew that it was start of something incredible. While the in-filled content is, naturally, not an exact match for what was originally there - especially obvious in the demonstration where entire objects, like rocks and bridges, are masked from the image and disappear entirely post-processing - it's surprisingly convincing and, unlike techniques which require post-processing for colour matching and other tweaks, entirely automatic in operation. The team's demonstration video, running on the company's Tesla V100 accelerator boards, showcases how the system operates, and it's undeniably impressive in use. The solution, as the paper's title makes clear, is the use of partial convolutions ' where the convolution is masked and renormalised to be conditioned on only valid pixels.' Coupled with a mechanism which generates an updated mask for the next layer as part of the forward pass, the result is a system which can automatically restore missing parts of images - even when there is nothing left in the original image from which a sample can be taken, as in one of the example uses of replacing a celebrity's eyes. Post-processing is usually used to reduce such artefacts, but are expensive and may fail.' ' This often leads to artefacts such as colour discrepancy and blurriness. ' Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value),' the researchers explain in the abstract for their paper, Image Inpainting for Irregular Holes using Partial Convolutions. Nvidia's research and development team have come up with a new GPU-accelerated deep-learning system capable of restoring images seemingly damaged beyond repair, building on existing 'image inpainting' technologies - functionally similar to Adobe Photoshop's Content Aware Fill tool - and, it claims, surpassing them. ![]()
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