Interactive Scene Authoring with Specialized Generative Primitives

Generating high-quality 3D digital assets often requires expert knowledge of complex design tools. We introduce Specialized Generative Primitives, a generative framework that allows non-expert users to author high-quality 3D scenes in a seamless, lightweight, and controllable manner. Each primitive is an efficient generative model that captures the distribution of a single exemplar from the real world. With our framework, users capture a video of an environment, which we turn into a high-quality and explicit appearance model thanks to 3D Gaussian Splatting. Users then select regions of interest guided by semantically-aware features. To create a generative primitive, we adapt Generative Cellular Automata to single-exemplar training and controllable generation. We decouple the generative task from the appearance model by operating on sparse voxels and we recover a high-quality output with a subsequent sparse patch consistency step. Each primitive can be trained within 10 minutes and used to author new scenes interactively in a fully compositional manner. We showcase interactive sessions where various primitives are extracted from real-world scenes and controlled to create 3D assets and scenes in a few minutes. We also demonstrate additional capabilities of our primitives: handling various 3D representations to control generation, transferring appearances, and editing geometries.

Neural Point Catacaustics for Novel-View Synthesis of Reflections

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/