S-NeRF: Neural Radiance Fields for Street Views Paid Members Public
In this paper, the authors propose a new street-view NeRF (S-NeRF) that considers novel view synthesis of both the large-scale background scenes and the foreground moving vehicles jointly. Learn more about their approach and the results of experiments!
Universal Guidance for Diffusion Models Paid Members Public
Typical diffusion models cannot be conditioned on other modalities without retraining. This work presents a universal guidance algorithm that enables diffusion models to be controlled by arbitrary guidance modalities without the need to retrain any use-specific components.
3D Generation on ImageNet Paid Members Public
In this paper, the authors develop a 3D generator with Generic Priors (3DGP): a 3D synthesis framework with more general assumptions about the training data, and show that it scales to challenging datasets, like ImageNet. It is based on three new ideas. Learn them!
3D-aware Conditional Image Synthesis Paid Members Public
This paper describes a 3D-aware conditional generative model for controllable photorealistic image synthesis. It integrates 3D representations with conditional generative modeling, i.e., enabling controllable high-resolution 3D-aware rendering by conditioning on user inputs.
SinMDM: Single Motion Diffusion Paid Members Public
This paper presents a Single Motion Diffusion Model, dubbed SinMDM, a model designed to learn the internal motifs of a single motion sequence with arbitrary topology and synthesize motions of arbitrary length that are faithful to them. Check it out!
LEGO-Net: Learning Regular Rearrangements of Objects in Rooms Paid Members Public
LEGO-Net is a data-driven transformer-based iterative method for LEarning reGular rearrangement of Objects in messy rooms. Results demonstrate that the method is able to reliably rearrange room scenes and outperform other methods. Find out more!
OmniObject3D Paid Members Public
OmniObject3D is a large vocabulary 3D object dataset featuring massive high-quality real-scanned 3D objects that can be used to facilitate the development of 3D perception, reconstruction, and generation in the real world. Give it a try!
MEGANE: Morphable Eyeglass and Avatar Network Paid Members Public
Megane is a 3D compositional morphable model of eyeglasses that incorporates high-fidelity geometric and photometric interaction effects. To support the variation in eyeglass topology, a hybrid representation of surface geometry and a volumetric representation is employed.