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Found 12 papers, 3 papers with code
MulSMo: Multimodal Stylized Motion Generation by Bidirectional Control Flow
Generating motion sequences conforming to a target style while adhering to the given content prompts requires accommodating both the content and style.
LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioning
For captioning, we finetune a large language model with the language-informative motion features to develop a strong motion captioning model.
MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling
Motion generation from discrete quantization offers many advantages over continuous regression, but at the cost of inevitable approximation errors.
GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulation
To facilitate geometry-aware guidance in physical property estimation, we introduce a novel hybrid framework that leverages 3D Gaussian representation to not only capture explicit shapes but also enable the simulated continuum to render object masks as 2D shape surrogates during training.
Freditor: High-Fidelity and Transferable NeRF Editing by Frequency Decomposition
This paper enables high-fidelity, transferable NeRF editing by frequency decomposition.
OV9D: Open-Vocabulary Category-Level 9D Object Pose and Size Estimation
Derived from OmniObject3D, OO3D-9D is the largest and most diverse dataset in the field of category-level object pose and size estimation.
Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation
We propose a novel synchronized generation and reconstruction method to effectively optimize the NeRF.
FS6D: Few-Shot 6D Pose Estimation of Novel Objects
6D object pose estimation networks are limited in their capability to scale to large numbers of object instances due to the close-set assumption and their reliance on high-fidelity object CAD models.
Towards Self-Supervised Category-Level Object Pose and Size Estimation
Instead, we propose a label-free method that learns to enforce the geometric consistency between category template mesh and observed object point cloud under a self-supervision manner.
iShape: A First Step Towards Irregular Shape Instance Segmentation
In this paper, we introduce a brand new dataset to promote the study of instance segmentation for objects with irregular shapes.
Ranked #1 on
Instance Segmentation
on iShape
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
Moreover, at the output representation stage, we designed a simple but effective 3D keypoints selection algorithm considering the texture and geometry information of objects, which simplifies keypoint localization for precise pose estimation.
Ranked #1 on
6D Pose Estimation
on LineMOD
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation.
Ranked #1 on
6D Pose Estimation using RGBD
on YCB-Video
(Mean ADD-S metric)
