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Found 17 papers, 7 papers with code
Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives
In contrast, the second-stage is the local diffusion, which parallelly generates detailed motion sequences under the guidance of the dance primitives and choreographic rules.
Ranked #1 on
Motion Synthesis
on FineDance
MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space Models
Gesture synthesis is a vital realm of human-computer interaction, with wide-ranging applications across various fields like film, robotics, and virtual reality.
Multi-Memory Matching for Unsupervised Visible-Infrared Person Re-Identification
To associate cross-modality clustered pseudo-labels, we design a Multi-Memory Learning and Matching (MMLM) module, ensuring that optimization explicitly focuses on the nuances of individual perspectives and establishes reliable cross-modality correspondences.
Text2Avatar: Text to 3D Human Avatar Generation with Codebook-Driven Body Controllable Attribute
Generating 3D human models directly from text helps reduce the cost and time of character modeling.
Exploring Multi-Modal Control in Music-Driven Dance Generation
Existing music-driven 3D dance generation methods mainly concentrate on high-quality dance generation, but lack sufficient control during the generation process.
Chain of Generation: Multi-Modal Gesture Synthesis via Cascaded Conditional Control
We introduce a novel method that separates priors from speech and employs multimodal priors as constraints for generating gestures.
Consistent123: One Image to Highly Consistent 3D Asset Using Case-Aware Diffusion Priors
However, due to utilizing the case-agnostic rigid strategy, their generalization ability to arbitrary cases and the 3D consistency of reconstruction are still poor.
UniHead: Unifying Multi-Perception for Detection Heads
More precisely, our approach (1) introduces deformation perception, enabling the model to adaptively sample object features; (2) proposes a Dual-axial Aggregation Transformer (DAT) to adeptly model long-range dependencies, thereby achieving global perception; and (3) devises a Cross-task Interaction Transformer (CIT) that facilitates interaction between the classification and localization branches, thus aligning the two tasks.
BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation
In light of this, we propose cross-modal learning under bird's-eye view for Domain Generalization (DG) of 3D semantic segmentation, called BEV-DG.
Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects
On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect.
Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping
It remains a challenging task since (1) it is hard to distinguish concealed objects from the background due to the intrinsic similarity and (2) the sparsely-annotated training data only provide weak supervision for model learning.
Efficient Converted Spiking Neural Network for 3D and 2D Classification
Spiking Neural Networks (SNNs) have attracted enormous research interest due to their low-power and biologically plausible nature.
Dual Pseudo-Labels Interactive Self-Training for Semi-Supervised Visible-Infrared Person Re-Identification
Visible-infrared person re-identification (VI-ReID) aims to match a specific person from a gallery of images captured from non-overlapping visible and infrared cameras.
Camouflaged Object Detection With Feature Decomposition and Edge Reconstruction
COD is a challenging task due to the intrinsic similarity of camouflaged objects with the background, as well as their ambiguous boundaries.
Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud
Firstly, we construct a pretext task, \textit{i. e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network.
FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation
To address these problems, we propose FineDance, which contains 14. 6 hours of music-dance paired data, with fine-grained hand motions, fine-grained genres (22 dance genres), and accurate posture.
Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation
In this way, the graph topology of the whole point cloud can be effectively established by the introduced auxiliary supervision, such that the information propagation between the labeled and unlabeled points will be realized.
