Search Results for author:
Found 15 papers, 7 papers with code
DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters
Recent advances in generative models have enabled high-quality 3D character reconstruction from multi-modal.
SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation
In this paper, we propose SRIF, a novel Semantic shape Registration framework based on diffusion-based Image morphing and Flow estimation.
Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony
For the former, we propose to pre-train on data regarding a fixed identity with neutral emotion, and defer the incorporation of customizable conditions (identity and emotion) to fine-tuning stage, which is boosted by our novel X-Adapter for parameter-efficient fine-tuning.
Unsupervised Non-Rigid Point Cloud Matching through Large Vision Models
In this paper, we propose a novel learning-based framework for non-rigid point cloud matching, which can be trained purely on point clouds without any correspondence annotation but also be extended naturally to partial-to-full matching.
DynamicTrack: Advancing Gigapixel Tracking in Crowded Scenes
In this paper, we introduce DynamicTrack, a dynamic tracking framework designed to address gigapixel tracking challenges in crowded scenes.
XScale-NVS: Cross-Scale Novel View Synthesis with Hash Featurized Manifold
We also introduce a novel dataset, namely GigaNVS, to benchmark cross-scale, high-resolution novel view synthesis of realworld large-scale scenes.
Beyond Talking -- Generating Holistic 3D Human Dyadic Motion for Communication
Furthermore, we introduce the HoCo holistic communication dataset, which is a valuable resource for future research.
OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning
Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure.
Spatially and Spectrally Consistent Deep Functional Maps
We first justify that under certain conditions, the learned maps, when represented in the spectral domain, are already cycle consistent.
Neural Intrinsic Embedding for Non-rigid Point Cloud Matching
As a primitive 3D data representation, point clouds are prevailing in 3D sensing, yet short of intrinsic structural information of the underlying objects.
RealGraph: A Multiview Dataset for 4D Real-world Context Graph Generation
The CGG task capitalizes on the calibrated multiview videos of a dynamic scene, and targets at recovering semantic information (coordination, trajectories and relationships) of the presented objects in the form of spatio-temporal context graph in 4D space.
Optical Neural Ordinary Differential Equations
Increasing the layer number of on-chip photonic neural networks (PNNs) is essential to improve its model performance.
Cross-Camera Deep Colorization
Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-temporal resolution and color depth in the single-camera imaging system.
EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation
In this paper, we consider the problem of reference-based video super-resolution(RefVSR), i. e., how to utilize a high-resolution (HR) reference frame to super-resolve a low-resolution (LR) video sequence.
Reference-based Video Super-Resolution
Video Super-Resolution
OperatorNet: Recovering 3D Shapes From Difference Operators
This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices.
