Nicolás Violante Grezzi

We address the problem of recovering the 3D kinematics of articulated objects from a monocular video. Rather than relying on long-term point tracking or wide-baseline matching, we propose a category-agnostic optimization framework that treats articulated object understanding as a primitive-fitting problem. Geometric primitives represent parts organized via joint constraints, naturally handling partial observations and occlusions inherent to real-world data. Our method recovers complex kinematics from single casual videos and we introduce two new benchmarks, AiP-synth and AiP-real, featuring significant camera movement and heavy occlusion.

We leverage repetitive elements in 3D scenes to improve novel view synthesis. Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have greatly improved novel view synthesis but renderings of unseen and occluded parts remain low-quality if the training views are not exhaustive enough. Our key observation is that our environment is often full of repetitive elements. We propose to leverage those repetitions to improve the reconstruction of low-quality parts of the scene due to poor coverage and occlusions. We propose a method that segments each repeated instance in a 3DGS reconstruction, registers them together, and allows information to be shared among instances. Our method improves the geometry while also accounting for appearance variations across instances. We demonstrate our method on a variety of synthetic and real scenes with typical repetitive elements, leading to a substantial improvement in the quality of novel view synthesis.

Recent work has demonstrated that Generative Adversarial Networks (GANs) can be trained to generate 3D content from 2D image collections, by synthesizing features for neural radiance field rendering. However, most such solutions generate radiance, with lighting entangled with materials. This results in unrealistic appearance, since lighting cannot be changed and view-dependent effects such as reflections do not move correctly with the viewpoint. In addition, many methods have difficulty for full, 360◦ rotations, since they are often designed for mainly front-facing scenes such as faces We introduce a new 3D GAN framework that addresses these shortcomings, allowing multi-view coherent 360◦ viewing and at the same time relighting for objects with shiny reflections, which we exemplify using a car dataset. The success of our solution stems from three main contributions. First, we estimate initial camera poses for a dataset of car images, and then learn to refine the distribution of camera parameters while training the GAN. Second, we propose an efficient Image-Based Lighting model, that we use in a 3D GAN to generate disentangled reflectance, as opposed to the radiance synthesized in most previous work. The material is used for physically-based rendering with a dataset of environment maps. Third, we improve the 3D GAN architecture compared to previous work and design a careful training strategy that allows effective disentanglement. Our model is the first that generatea variety of 3D cars that are multi-view consistent and that can be relit interactively with any environment map.