Search Results for author:
Found 16 papers, 6 papers with code
GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation
We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from sparse-view images in around 0. 1s.
Real-time 3D-aware Portrait Editing from a Single Image
This work presents 3DPE, a practical method that can efficiently edit a face image following given prompts, like reference images or text descriptions, in a 3D-aware manner.
Learning Naturally Aggregated Appearance for Efficient 3D Editing
Neural radiance fields, which represent a 3D scene as a color field and a density field, have demonstrated great progress in novel view synthesis yet are unfavorable for editing due to the implicitness.
GenDeF: Learning Generative Deformation Field for Video Generation
We offer a new perspective on approaching the task of video generation.
Gaussian Shell Maps for Efficient 3D Human Generation
Instead of rasterizing the shells directly, we sample 3D Gaussians on the shells whose attributes are encoded in the texture features.
DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model
We propose \textbf{DMV3D}, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion.
Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis
Due to the difficulty in scaling up, generative adversarial networks (GANs) seem to be falling from grace on the task of text-conditioned image synthesis.
Learning 3D-aware Image Synthesis with Unknown Pose Distribution
Existing methods for 3D-aware image synthesis largely depend on the 3D pose distribution pre-estimated on the training set.
LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis
This work presents an easy-to-use regularizer for GAN training, which helps explicitly link some axes of the latent space to a set of pixels in the synthesized image.
DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis
Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of objects.
Deep Generative Models on 3D Representations: A Survey
In this survey, we thoroughly review the ongoing developments of 3D generative models, including methods that employ 2D and 3D supervision.
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator
We argue that, considering the two-player game in the formulation of GANs, only making the generator 3D-aware is not enough.
3D-Aware Indoor Scene Synthesis with Depth Priors
In this way, the discriminator can take the spatial arrangement into account and advise the generator to learn an appropriate depth condition.
Stereo Waterdrop Removal with Row-wise Dilated Attention
Thus, we propose a learning-based model for waterdrop removal with stereo images.
Neural Camera Simulators
To facilitate the learning of a simulator model, we collect a dataset of the 10, 000 raw images of 450 scenes with different exposure settings.
Predicting college basketball match outcomes using machine learning techniques: some results and lessons learned
Most existing work on predicting NCAAB matches has been developed in a statistical context.
