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Found 36 papers, 13 papers with code
DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh
Text-driven avatar generation has gained significant attention owing to its convenience.
UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views
In neural rendering, we design a base-adaptive NeRF network to exploit the uncertainty estimation for explicitly handling the photometric inconsistencies.
MagicMan: Generative Novel View Synthesis of Humans with 3D-Aware Diffusion and Iterative Refinement
Existing works in single-image human reconstruction suffer from weak generalizability due to insufficient training data or 3D inconsistencies for a lack of comprehensive multi-view knowledge.
GRAPE: Generalizable and Robust Multi-view Facial Capture
Deep learning-based multi-view facial capture methods have shown impressive accuracy while being several orders of magnitude faster than a traditional mesh registration pipeline.
Free-SurGS: SfM-Free 3D Gaussian Splatting for Surgical Scene Reconstruction
To tackle this problem, in this paper, we propose the first SfM-free 3DGS-based method for surgical scene reconstruction by jointly optimizing the camera poses and scene representation.
MeGA: Hybrid Mesh-Gaussian Head Avatar for High-Fidelity Rendering and Head Editing
In this paper, we propose a Hybrid Mesh-Gaussian Head Avatar (MeGA) that models different head components with more suitable representations.
Advances in 3D Generation: A Survey
In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications.
TIP-Editor: An Accurate 3D Editor Following Both Text-Prompts And Image-Prompts
To this end, we propose a 3D scene editing framework, TIPEditor, that accepts both text and image prompts and a 3D bounding box to specify the editing region.
PHRIT: Parametric Hand Representation with Implicit Template
We propose PHRIT, a novel approach for parametric hand mesh modeling with an implicit template that combines the advantages of both parametric meshes and implicit representations.
Neural Point-based Volumetric Avatar: Surface-guided Neural Points for Efficient and Photorealistic Volumetric Head Avatar
Rendering photorealistic and dynamically moving human heads is crucial for ensuring a pleasant and immersive experience in AR/VR and video conferencing applications.
Skinned Motion Retargeting with Residual Perception of Motion Semantics & Geometry
Driven by our explored distance-based losses that explicitly model the motion semantics and geometry, these two modules can learn residual motion modifications on the source motion to generate plausible retargeted motion in a single inference without post-processing.
High-level Feature Guided Decoding for Semantic Segmentation
One crucial design of the HFG is to protect the high-level features from being contaminated by using proper stop-gradient operations so that the backbone does not update according to the noisy gradient from the upsampler.
Get3DHuman: Lifting StyleGAN-Human into a 3D Generative Model using Pixel-aligned Reconstruction Priors
Specifically, we bridge the latent space of Get3DHuman with that of StyleGAN-Human via a specially-designed prior network, where the input latent code is mapped to the shape and texture feature volumes spanned by the pixel-aligned 3D reconstructor.
Audio2Gestures: Generating Diverse Gestures from Audio
Finally, we demonstrate that our method can be readily used to generate motion sequences with user-specified motion clips on the timeline.
Learning Audio-Driven Viseme Dynamics for 3D Face Animation
We show that the predicted viseme curves can be applied to different viseme-rigged characters to yield various personalized animations with realistic and natural facial motions.
CARD: Semantic Segmentation with Efficient Class-Aware Regularized Decoder
Extensive experiments and ablation studies conducted on multiple benchmark datasets demonstrate that the proposed CAR can boost the accuracy of all baseline models by up to 2. 23% mIOU with superior generalization ability.
FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction
Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches to generate multi-view normalized face images from single-image inputs.
Ranked #3 on
3D Face Reconstruction
on REALY
NEURAL MARIONETTE: A Transformer-based Multi-action Human Motion Synthesis System
Along with the novel system, we also present a new dataset dedicated to the multi-action motion synthesis task, which contains both action tags and their contextual information.
Learning to Construct 3D Building Wireframes from 3D Line Clouds
The network takes a line cloud as input , i. e., a nonstructural and unordered set of 3D line segments extracted from multi-view images, and outputs a 3D wireframe of the underlying building, which consists of a sparse set of 3D junctions connected by line segments.
PIFu for the Real World: A Self-supervised Framework to Reconstruct Dressed Human from Single-view Images
On synthetic data, our Intersection-Over-Union (IoU) achieves to 93. 5%, 18% higher compared with PIFuHD.
Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds
To guide the network quickly fit the coarse shape, we propose to utilize the signed supervision in regions that are obviously outside the object and can be easily determined, resulting in our semi-signed supervision.
REALY: Rethinking the Evaluation of 3D Face Reconstruction
The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan.
CAR: Class-aware Regularizations for Semantic Segmentation
Recent segmentation methods, such as OCR and CPNet, utilizing "class level" information in addition to pixel features, have achieved notable success for boosting the accuracy of existing network modules.
Ranked #8 on
Semantic Segmentation
on PASCAL Context
Consistent 3D Hand Reconstruction in Video via self-supervised Learning
We present a method for reconstructing accurate and consistent 3D hands from a monocular video.
NeRFReN: Neural Radiance Fields with Reflections
Specifically, we propose to split a scene into transmitted and reflected components, and model the two components with separate neural radiance fields.
Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders
In order to overcome this problem, we propose a novel conditional variational autoencoder (VAE) that explicitly models one-to-many audio-to-motion mapping by splitting the cross-modal latent code into shared code and motion-specific code.
Ranked #3 on
Gesture Generation
on BEAT
Animatable Neural Radiance Fields from Monocular RGB Videos
We present animatable neural radiance fields (animatable NeRF) for detailed human avatar creation from monocular videos.
Model-based 3D Hand Reconstruction via Self-Supervised Learning
For the first time, we demonstrate the feasibility of training an accurate 3D hand reconstruction network without relying on manual annotations.
Ranked #8 on
3D Hand Pose Estimation
on HO-3D v3
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation.
Ranked #6 on
Semantic Segmentation
on COCO-Stuff test
High-Fidelity 3D Digital Human Head Creation from RGB-D Selfies
We present a fully automatic system that can produce high-fidelity, photo-realistic 3D digital human heads with a consumer RGB-D selfie camera.
Joint Hand-object 3D Reconstruction from a Single Image with Cross-branch Feature Fusion
In this work, we propose to consider hand and object jointly in feature space and explore the reciprocity of the two branches.
Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes
While people tracking has been greatly improved over the recent years, crowd scenes remain particularly challenging for people tracking due to heavy occlusions, high crowd density, and significant appearance variation.
Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid
In this paper, in contrast to using filters of different sizes, we utilize an image pyramid to deal with scale variations.
Incorporating Side Information by Adaptive Convolution
For example, for crowd counting, the camera perspective (e. g., camera angle and height) gives a clue about the appearance and scale of people in the scene.
Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking
The goal of this paper is to evaluate density maps generated by density estimation methods on a variety of crowd analysis tasks, including counting, detection, and tracking.
Crowd Counting by Adapting Convolutional Neural Networks with Side Information
In order to incorporate the available side information, we propose an adaptive convolutional neural network (ACNN), where the convolutional filter weights adapt to the current scene context via the side information.
