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Found 32 papers, 16 papers with code
FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization
3D Gaussian splatting has achieved very impressive performance in real-time novel view synthesis.
WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation
Generating multi-camera street-view videos is critical for augmenting autonomous driving datasets, addressing the urgent demand for extensive and varied data.
Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
Instead, our approach BOSS (BOotStrapping your own Skills) learns to accomplish new tasks by performing "skill bootstrapping," where an agent with a set of primitive skills interacts with the environment to practice new skills without receiving reward feedback for tasks outside of the initial skill set.
Pose-Free Neural Radiance Fields via Implicit Pose Regularization
However, as the pose estimator is trained with only rendered images, the pose estimation is usually biased or inaccurate for real images due to the domain gap between real images and rendered images, leading to poor robustness for the pose estimation of real images and further local minima in joint optimization.
WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields
Neural Radiance Field (NeRF) has shown impressive performance in novel view synthesis via implicit scene representation.
SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling
Pre-training robot policies with a rich set of skills can substantially accelerate the learning of downstream tasks.
Weakly Supervised 3D Open-vocabulary Segmentation
Open-vocabulary segmentation of 3D scenes is a fundamental function of human perception and thus a crucial objective in computer vision research.
POCE: Pose-Controllable Expression Editing
POCE achieves the more accessible and realistic pose-controllable expression editing by mapping face images into UV space, where facial expressions and head poses can be disentangled and edited separately.
Audio-Driven Talking Face Generation with Diverse yet Realistic Facial Animations
To accommodate fair variation of plausible facial animations for the same audio, we design a transformer-based probabilistic mapping network that can model the variational facial animation distribution conditioned upon the input audio and autoregressively convert the audio signals into a facial animation sequence.
StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields
In addition, it transforms the grid features according to the reference style which directly leads to high-quality zero-shot style transfer.
Regularized Vector Quantization for Tokenized Image Synthesis
The first is a prior distribution regularization which measures the discrepancy between a prior token distribution and the predicted token distribution to avoid codebook collapse and low codebook utilization.
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution
In addition, stochastic noises fed to the generator are employed for unconditional detail generation, which tends to produce unfaithful details that compromise the fidelity of the generated SR image.
RenderNet: Visual Relocalization Using Virtual Viewpoints in Large-Scale Indoor Environments
Visual relocalization has been a widely discussed problem in 3D vision: given a pre-constructed 3D visual map, the 6 DoF (Degrees-of-Freedom) pose of a query image is estimated.
Auto-regressive Image Synthesis with Integrated Quantization
Extensive experiments over multiple conditional image generation tasks show that our method achieves superior diverse image generation performance qualitatively and quantitatively as compared with the state-of-the-art.
VMRF: View Matching Neural Radiance Fields
With the feature transport plan as the guidance, a novel pose calibration technique is designed which rectifies the initially randomized camera poses by predicting relative pose transformations between the pair of rendered and real images.
Towards Counterfactual Image Manipulation via CLIP
In addition, we design a simple yet effective scheme that explicitly maps CLIP embeddings (of target text) to the latent space and fuses them with latent codes for effective latent code optimization and accurate editing.
Marginal Contrastive Correspondence for Guided Image Generation
We design a Marginal Contrastive Learning Network (MCL-Net) that explores contrastive learning to learn domain-invariant features for realistic exemplar-based image translation.
Modulated Contrast for Versatile Image Synthesis
Perceiving the similarity between images has been a long-standing and fundamental problem underlying various visual generation tasks.
QuadTree Attention for Vision Transformers
Transformers have been successful in many vision tasks, thanks to their capability of capturing long-range dependency.
An Intelligent Self-driving Truck System For Highway Transportation
Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry.
Multimodal Image Synthesis and Editing: The Generative AI Era
With superb power in modeling the interaction among multimodal information, multimodal image synthesis and editing has become a hot research topic in recent years.
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing
Recent years pretrained language models (PLMs) hit a success on several downstream tasks, showing their power on modeling language.
Learning to Match Features with Seeded Graph Matching Network
2) Seeded Graph Neural Network, which utilizes seed matches to pass messages within/across images and predicts assignment costs.
Bi-level Feature Alignment for Versatile Image Translation and Manipulation
This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance in image generation by explicitly building a correspondence.
Blind Image Super-Resolution via Contrastive Representation Learning
Extensive experiments on synthetic datasets and real images show that the proposed CRL-SR can handle multi-modal and spatially variant degradation effectively under blind settings and it also outperforms state-of-the-art SR methods qualitatively and quantitatively.
KFNet: Learning Temporal Camera Relocalization using Kalman Filtering
Temporal camera relocalization estimates the pose with respect to each video frame in sequence, as opposed to one-shot relocalization which focuses on a still image.
ASLFeat: Learning Local Features of Accurate Shape and Localization
This work focuses on mitigating two limitations in the joint learning of local feature detectors and descriptors.
Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency
The self-supervised learning of depth and pose from monocular sequences provides an attractive solution by using the photometric consistency of nearby frames as it depends much less on the ground-truth data.
Learning Two-View Correspondences and Geometry Using Order-Aware Network
First, to capture the local context of sparse correspondences, the network clusters unordered input correspondences by learning a soft assignment matrix.
Efficient Semantic Scene Completion Network with Spatial Group Convolution
We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks.
Ranked #9 on
3D Semantic Scene Completion
on SemanticKITTI
ContextDesc: Local Descriptor Augmentation with Cross-Modality Context
Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations.
Reconstruction and Registration of Large-Scale Medical Scene Using Point Clouds Data from Different Modalities
In this paper, we proposed a method which can fuse different modalities 3D data to get a large-scale and dense point cloud.
