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Found 19 papers, 12 papers with code
CNS-Edit: 3D Shape Editing via Coupled Neural Shape Optimization
First, we design the coupled neural shape (CNS) representation for supporting 3D shape editing.
Make-A-Shape: a Ten-Million-scale 3D Shape Model
We then make the representation generatable by a diffusion model by devising the subband coefficients packing scheme to layout the representation in a low-resolution grid.
EXIM: A Hybrid Explicit-Implicit Representation for Text-Guided 3D Shape Generation
This paper presents a new text-guided technique for generating 3D shapes.
Texture Generation on 3D Meshes with Point-UV Diffusion
In this work, we focus on synthesizing high-quality textures on 3D meshes.
CLIPXPlore: Coupled CLIP and Shape Spaces for 3D Shape Exploration
This paper presents CLIPXPlore, a new framework that leverages a vision-language model to guide the exploration of the 3D shape space.
You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding
3D scene understanding, e. g., point cloud semantic and instance segmentation, often requires large-scale annotated training data, but clearly, point-wise labels are too tedious to prepare.
DreamStone: Image as Stepping Stone for Text-Guided 3D Shape Generation
The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view reconstruction (SVR) model to map CLIP features to shapes: to begin with, map the CLIP image feature to the detail-rich 3D shape space of the SVR model, then map the CLIP text feature to the 3D shape space through encouraging the CLIP-consistency between rendered images and the input text.
Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation
This paper presents a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain.
Command-Driven Articulated Object Understanding and Manipulation
The key of Cart is to utilize the prediction of object structures to connect visual observations with user commands for effective manipulations.
MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
Weakly supervised detection of anomalies in surveillance videos is a challenging task.
Anomaly Detection In Surveillance Videos
Video Anomaly Detection
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection
Importantly, we formulate the lightweight plug-in S2D module and the point cloud reconstruction module in SDet to densify 3D features and train SDet to produce 3D features, following the dense 3D features in DDet.
ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation
Text-guided 3D shape generation remains challenging due to the absence of large paired text-shape data, the substantial semantic gap between these two modalities, and the structural complexity of 3D shapes.
Towards Implicit Text-Guided 3D Shape Generation
In this work, we explore the challenging task of generating 3D shapes from text.
TWIST: Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance Segmentation
We explore the way to alleviate the label-hungry problem in a semi-supervised setting for 3D instance segmentation.
One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation
Point cloud semantic segmentation often requires largescale annotated training data, but clearly, point-wise labels are too tedious to prepare.
3D-to-2D Distillation for Indoor Scene Parsing
First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training, so the 2D network can infer without requiring 3D data.
GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation
Note that GeoNet++ is generic and can be used in other depth/normal prediction frameworks to improve the quality of 3D reconstruction and pixel-wise accuracy of depth and surface normals.
Global Texture Enhancement for Fake Face Detection in the Wild
In this paper, we conduct an empirical study on fake/real faces, and have two important observations: firstly, the texture of fake faces is substantially different from real ones; secondly, global texture statistics are more robust to image editing and transferable to fake faces from different GANs and datasets.
GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation
In this paper, we propose Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image.
