6 accepted papers
We present Readout Guidance a method for controlling text-to-image diffusion models with learned signals. Readout Guidance uses readout heads lightweight networks trained to extract signals from the features of a pre-trained frozen diffusion model at every timestep. These readouts can encode single-…
Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance for upcoming applications in AR or VR. These range from mixed reality applications for teleconferencing, virtual measuring, virtual room planing, to robotic applications. While current volume-based view synthes…
We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones. Our approach augments neural radiance fields (NeRF) by optimizing an additional continuous volumetric deformation field that warps each observed point…
Accurate modeling of 3D objects exhibiting transparency, reflections and thin structures is an extremely challenging problem. Inspired by billboards and geometric proxies used in computer graphics, this paper proposes Generative Latent Textured Objects (GeLaTO), a compact representation that combine…
We explore total scene capture --- recording, modeling, and rerendering a scene under varying appearance such as season and time of day. Starting from Internet photos of a tourist landmark, we apply traditional 3D reconstruction to register the photos and approximate the scene as a point cloud. For…
We propose a new computer vision task we call "distractor prediction." Distractors are the regions of an image that draw attention away from the main subjects and reduce the overall image quality. Removing distractors --- for example, using in-painting --- can improve the composition of an image. In…