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Found 62 papers, 27 papers with code
SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches
Sketch-based image retrieval (SBIR) has been a popular research topic in recent years.
Tailor: An Integrated Text-Driven CG-Ready Human and Garment Generation System
Next, we develop topology-preserving deformation with novel geometric losses to adapt garments precisely to body geometries.
qNBO: quasi-Newton Meets Bilevel Optimization
Bilevel optimization, addressing challenges in hierarchical learning tasks, has gained significant interest in machine learning.
Towards Sharper Information-theoretic Generalization Bounds for Meta-Learning
In recent years, information-theoretic generalization bounds have emerged as a promising approach for analyzing the generalization capabilities of meta-learning algorithms.
Towards Better Robustness: Progressively Joint Pose-3DGS Learning for Arbitrarily Long Videos
3D Gaussian Splatting (3DGS) has emerged as a powerful representation due to its efficiency and high-fidelity rendering.
Weighted Poisson-disk Resampling on Large-Scale Point Clouds
For large-scale point cloud processing, resampling takes the important role of controlling the point number and density while keeping the geometric consistency.
AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos
We introduce AlphaTablets, a novel and generic representation of 3D planes that features continuous 3D surface and precise boundary delineation.
StdGEN: Semantic-Decomposed 3D Character Generation from Single Images
We present StdGEN, an innovative pipeline for generating semantically decomposed high-quality 3D characters from single images, enabling broad applications in virtual reality, gaming, and filmmaking, etc.
MonoPlane: Exploiting Monocular Geometric Cues for Generalizable 3D Plane Reconstruction
This paper presents a generalizable 3D plane detection and reconstruction framework named MonoPlane.
SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset
Reconstructing accurate 3D surfaces for street-view scenarios is crucial for applications such as digital entertainment and autonomous driving simulation.
Rectified Diffusion Guidance for Conditional Generation
Classifier-Free Guidance (CFG), which combines the conditional and unconditional score functions with two coefficients summing to one, serves as a practical technique for diffusion model sampling.
LLM-based Optimization of Compound AI Systems: A Survey
This paper presents a survey of the principles and emerging trends in LLM-based optimization of compound AI systems.
LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition
EEG-based emotion recognition (EER) has gained significant attention due to its potential for understanding and analyzing human emotions.
PVP-Recon: Progressive View Planning via Warping Consistency for Sparse-View Surface Reconstruction
Neural implicit representations have revolutionized dense multi-view surface reconstruction, yet their performance significantly diminishes with sparse input views.
YOLOO: You Only Learn from Others Once
YOLOO empowers the point cloud encoder to learn a unified tri-modal representation (UTR) from point clouds and other modalities, such as images and textual cues, all at once.
Gaussian in the Dark: Real-Time View Synthesis From Inconsistent Dark Images Using Gaussian Splatting
3D Gaussian Splatting has recently emerged as a powerful representation that can synthesize remarkable novel views using consistent multi-view images as input.
NGP-RT: Fusing Multi-Level Hash Features with Lightweight Attention for Real-Time Novel View Synthesis
Although it achieves fast training speed, there is still a lot of room for improvement in its rendering speed due to the per-point MLP executions for implicit multi-level feature aggregation, especially for real-time applications.
DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraints
Recent advances in large language model assistants have made them indispensable, raising significant concerns over managing their safety.
Exploring Text-to-Motion Generation with Human Preference
This paper presents an exploration of preference learning in text-to-motion generation.
TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data
Current methods predominantly focus on image and text data or address artificially introduced noise, neglecting the complexities of natural noise in time-series analysis.
Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal Patterns
Interpersonal relationship quality is pivotal in social and occupational contexts.
Towards More Accurate Diffusion Model Acceleration with A Timestep Tuner
A diffusion model which is formulated to produce an image using thousands of denoising steps usually suffers from a slow inference speed.
Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation
By lifting the pre-trained 2D diffusion models into Neural Radiance Fields (NeRFs), text-to-3D generation methods have made great progress.
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment
Deep neural network models have achieved remarkable progress in 3D scene understanding while trained in the closed-set setting and with full labels.
SMaRt: Improving GANs with Score Matching Regularity
In this work, we revisit the mathematical foundations of GANs, and theoretically reveal that the native adversarial loss for GAN training is insufficient to fix the problem of subsets with positive Lebesgue measure of the generated data manifold lying out of the real data manifold.
Augmenting Unsupervised Reinforcement Learning with Self-Reference
Our approach achieves state-of-the-art results in terms of Interquartile Mean (IQM) performance and Optimality Gap reduction on the Unsupervised Reinforcement Learning Benchmark for model-free methods, recording an 86% IQM and a 16% Optimality Gap.
Towards More Accurate Diffusion Model Acceleration with A Timestep Aligner
By viewing the generation of diffusion models as a discretized integrating process, we argue that the quality drop is partly caused by applying an inaccurate integral direction to a timestep interval.
T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation
Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF.
MMPI: a Flexible Radiance Field Representation by Multiple Multi-plane Images Blending
In this paper, we explore the potential of MPI and show that MPI can synthesize high-quality novel views of complex scenes with diverse camera distributions and view directions, which are not only limited to simple forward-facing scenes.
DiffPoseTalk: Speech-Driven Stylistic 3D Facial Animation and Head Pose Generation via Diffusion Models
The generation of stylistic 3D facial animations driven by speech presents a significant challenge as it requires learning a many-to-many mapping between speech, style, and the corresponding natural facial motion.
FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization
We propose a cross-modality point cloud registration framework FF-LOGO: a cross-modality point cloud registration method with feature filtering and local-global optimization.
Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement
To enhance the normal priors, we introduce a simple yet effective image sharpening and denoising technique, coupled with a network that estimates the pixel-wise uncertainty of the predicted surface normal vectors.
ExpeL: LLM Agents Are Experiential Learners
The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs.
O$^2$-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene with a Pre-trained 2D Diffusion Model
In this paper, we propose a novel framework, empowered by a 2D diffusion-based in-painting model, to reconstruct complete surfaces for the hidden parts of objects.
Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos
In this paper, we propose an altering resolution framework called AR-Seg for compressed videos to achieve efficient VSS.
A Mixture of Surprises for Unsupervised Reinforcement Learning
However, both strategies rely on a strong assumption: the entropy of the environment's dynamics is either high or low.
PCKRF: Point Cloud Completion and Keypoint Refinement With Fusion Data for 6D Pose Estimation
In this paper, we propose Point Cloud Completion and Keypoint Refinement with Fusion Data (PCKRF), a new pose refinement pipeline for 6D pose estimation.
Continuously Controllable Facial Expression Editing in Talking Face Videos
In this paper, we propose a high-quality facial expression editing method for talking face videos, allowing the user to control the target emotion in the edited video continuously.
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild
In addition, our method is able to retain reasonable accuracy of camera poses on fully static scenes, which consistently outperforms strong state-of-the-art dense correspondence based methods with end-to-end deep learning, demonstrating the potential of dense indirect methods based on optical flow and point trajectories.
Dynamic Neural Textures: Generating Talking-Face Videos with Continuously Controllable Expressions
In this paper, we propose a method to generate talking-face videos with continuously controllable expressions in real-time.
PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows
By considering noisy point clouds as a joint distribution of clean points and noise, the denoised results can be derived from disentangling the noise counterpart from latent point representation, and the mapping between Euclidean and latent spaces is modeled by normalizing flows.
Quality Metric Guided Portrait Line Drawing Generation from Unpaired Training Data
In this paper, we propose a novel method to automatically transform face photos to portrait drawings using unpaired training data with two new features; i. e., our method can (1) learn to generate high quality portrait drawings in multiple styles using a single network and (2) generate portrait drawings in a "new style" unseen in the training data.
Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms
Unlike the conventional facial expressions, micro-expressions are involuntary and transient facial expressions capable of revealing the genuine emotions that people attempt to hide.
A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo
Experimental results on ScanNet and RGB-D Scenes V2 demonstrate state-of-the-art performance of the proposed deep MVS system on multi-view depth estimation, with our proposed solver consistently improving the depth quality over both conventional and deep learning based MVS pipelines.
Audio-Driven Talking Face Video Generation with Dynamic Convolution Kernels
In this paper, we present a dynamic convolution kernel (DCK) strategy for convolutional neural networks.
E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches
We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness.
PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows
Point cloud upsampling aims to generate dense point clouds from given sparse ones, which is a challenging task due to the irregular and unordered nature of point sets.
Autoregressive Stylized Motion Synthesis With Generative Flow
Motion style transfer is an important problem in many computer graphics and computer vision applications, including human animation, games, and robotics.
AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis
Generating high-fidelity talking head video by fitting with the input audio sequence is a challenging problem that receives considerable attentions recently.
NPRportrait 1.0: A Three-Level Benchmark for Non-Photorealistic Rendering of Portraits
Despite the recent upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer, the state of performance evaluation in this field is limited, especially compared to the norms in the computer vision and machine learning communities.
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
In this work, we tackle the essential problem of scale inconsistency for self-supervised joint depth-pose learning.
Learning to Accelerate Decomposition for Multi-Directional 3D Printing
Different printing directions are employed in different regions to fabricate a model with tremendously less support (or even no support in many cases). To obtain optimized decomposition, a large beam width needs to be used in the search algorithm, leading to a very time-consuming computation.
3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Face Photos
The most challenging issue for our system is that the source domain of face photos (characterized by normal 2D faces) is significantly different from the target domain of 3D caricatures (characterized by 3D exaggerated face shapes and textures).
STD-Net: Structure-preserving and Topology-adaptive Deformation Network for 3D Reconstruction from a Single Image
3D reconstruction from a single view image is a long-standing prob-lem in computer vision.
Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose
In this paper, we address this problem by proposing a deep neural network model that takes an audio signal A of a source person and a very short video V of a target person as input, and outputs a synthesized high-quality talking face video with personalized head pose (making use of the visual information in V), expression and lip synchronization (by considering both A and V).
A Configuration-Space Decomposition Scheme for Learning-based Collision Checking
Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution.
Attention-aware Multi-stroke Style Transfer
Neural style transfer has drawn considerable attention from both academic and industrial field.
Efficient sparse semismooth Newton methods for the clustered lasso problem
Based on the new formulation, we derive an efficient procedure for its computation.
CartoonGAN: Generative Adversarial Networks for Photo Cartoonization
Two novel losses suitable for cartoonization are proposed: (1) a semantic content loss, which is formulated as a sparse regularization in the high-level feature maps of the VGG network to cope with substantial style variation between photos and cartoons, and (2) an edge-promoting adversarial loss for preserving clear edges.
Content-Sensitive Supervoxels via Uniform Tessellations on Video Manifolds
We propose an efficient Lloyd-like method with a splitting-merging scheme to compute a uniform tessellation on M, which induces the CSS in X. Theoretically our method has a good competitive ratio O(1).
Learning to Rank Retargeted Images
Observing that it is challenging even for human subjects to give consistent scores for retargeting results of different source images, in this paper we propose a learning-based OQA method that predicts the ranking of a set of retargeted images with the same source image.
Manifold SLIC: A Fast Method to Compute Content-Sensitive Superpixels
Superpixels are perceptually meaningful atomic regions that can effectively capture image features.
