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Found 17 papers, 11 papers with code
InTeX: Interactive Text-to-texture Synthesis via Unified Depth-aware Inpainting
Text-to-texture synthesis has become a new frontier in 3D content creation thanks to the recent advances in text-to-image models.
3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors
Specifically, it is powered by a text-conditioned tri-plane latent diffusion model, which quickly generates coarse 3D samples for fast prototyping.
LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation
2) 3D Backbone: We present an asymmetric U-Net as a high-throughput backbone operating on multi-view images, which can be produced from text or single-view image input by leveraging multi-view diffusion models.
DreamGaussian4D: Generative 4D Gaussian Splatting
Remarkable progress has been made in 4D content generation recently.
HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting
In this paper, we propose an efficient yet effective framework, HumanGaussian, that generates high-quality 3D humans with fine-grained geometry and realistic appearance.
DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation
In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks.
TeCH: Text-guided Reconstruction of Lifelike Clothed Humans
But how to effectively capture all visual attributes of an individual from a single image, which are sufficient to reconstruct unseen areas (e. g., the back view)?
Interactive Segment Anything NeRF with Feature Imitation
We propose to imitate the backbone feature of off-the-shelf perception models to achieve zero-shot semantic segmentation with NeRF.
Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement
Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction.
Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition
While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their potential usage.
Secure Embedding Aggregation for Federated Representation Learning
We consider a federated representation learning framework, where with the assistance of a central server, a group of $N$ distributed clients train collaboratively over their private data, for the representations (or embeddings) of a set of entities (e. g., users in a social network).
Compressible-composable NeRF via Rank-residual Decomposition
To circumvent the hurdle, in this paper, we present an explicit neural field representation that enables efficient and convenient manipulation of models.
Point Scene Understanding via Disentangled Instance Mesh Reconstruction
Semantic scene reconstruction from point cloud is an essential and challenging task for 3D scene understanding.
Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspective
Firstly, we transfer the voxelized scenes to point clouds by removing these visible empty voxels and adopt a deep point stream to capture semantic information from the scene efficiently.
Ranked #4 on
3D Semantic Scene Completion
on NYUv2
Joint Implicit Image Function for Guided Depth Super-Resolution
Inspired by the recent progress in implicit neural representation, we propose to formulate the guided super-resolution as a neural implicit image interpolation problem, where we take the form of a general image interpolation but use a novel Joint Implicit Image Function (JIIF) representation to learn both the interpolation weights and values.
Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural Networks
In particular, we cast the graph optimization problem as distance metric learning to capture pairwise similarities of features in each layer.
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression
In this paper, we represent skeletons naturally on graphs, and propose a graph regression based GCN (GR-GCN) for skeleton-based action recognition, aiming to capture the spatio-temporal variation in the data.
Ranked #2 on
Skeleton Based Action Recognition
on Florence 3D
