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Found 40 papers, 13 papers with code
SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing
Specifically, we first employ three text-conditioned 3D generative models to generate garment mesh, body shape and hair strands from the given text prompt.
COIN: Control-Inpainting Diffusion Prior for Human and Camera Motion Estimation
Experiments on three challenging benchmarks demonstrate the effectiveness of COIN, which outperforms the state-of-the-art methods in terms of global human motion estimation and camera motion estimation.
Data Exposure from LLM Apps: An In-depth Investigation of OpenAI's GPTs
Our measurements indicate that the disclosures for most of the collected data types are omitted in privacy policies, with only 5. 8% of Actions clearly disclosing their data collection practices.
SecGPT: An Execution Isolation Architecture for LLM-Based Systems
These LLM apps leverage the de facto natural language-based automated execution paradigm of LLMs: that is, apps and their interactions are defined in natural language, provided access to user data, and allowed to freely interact with each other and the system.
Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation
To generate composite animations from a multi-track timeline, we propose a new test-time denoising method.
What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering.
GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning
Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations.
PACE: Human and Camera Motion Estimation from in-the-wild Videos
This design combines the strengths of SLAM and motion priors, which leads to significant improvements in human and camera motion estimation.
LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT Plugins
In this paper, we propose a framework that lays a foundation for LLM platform designers to analyze and improve the security, privacy, and safety of current and future third-party integrated LLM platforms.
PURL: Safe and Effective Sanitization of Link Decoration
While privacy-focused browsers have taken steps to block third-party cookies and mitigate browser fingerprinting, novel tracking techniques that can bypass existing countermeasures continue to emerge.
Generalizable One-shot Neural Head Avatar
We present a method that reconstructs and animates a 3D head avatar from a single-view portrait image.
Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization
To tackle this problem, we propose a novel method for constructing implicit 3D morphable face models that are both generalizable and intuitive for editing.
Learning Human Dynamics in Autonomous Driving Scenarios
In this work, we propose a holistic framework for learning physically plausible human dynamics from real driving scenarios, narrowing the gap between real and simulated human behavior in safety-critical applications.
RANA: Relightable Articulated Neural Avatars
We propose RANA, a relightable and articulated neural avatar for the photorealistic synthesis of humans under arbitrary viewpoints, body poses, and lighting.
PhysDiff: Physics-Guided Human Motion Diffusion Model
Specifically, we propose a physics-based motion projection module that uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically-plausible motion.
DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars
In this work, we present, DRaCoN, a framework for learning full-body volumetric avatars which exploits the advantages of both the 2D and 3D neural rendering techniques.
Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects
Rendering articulated objects while controlling their poses is critical to applications such as virtual reality or animation for movies.
GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras
Since the joint reconstruction of human motions and camera poses is underconstrained, we propose a global trajectory predictor that generates global human trajectories based on local body movements.
Ranked #1 on
Global 3D Human Pose Estimation
on EMDB
Self-Supervised Object Detection via Generative Image Synthesis
We present SSOD, the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection.
Physics-based Human Motion Estimation and Synthesis from Videos
By enabling learning of motion synthesis from video, our method paves the way for large-scale, realistic and diverse motion synthesis.
Adversarial Motion Modelling helps Semi-supervised Hand Pose Estimation
Hand pose estimation is difficult due to different environmental conditions, object- and self-occlusion as well as diversity in hand shape and appearance.
Weakly-Supervised Physically Unconstrained Gaze Estimation
A major challenge for physically unconstrained gaze estimation is acquiring training data with 3D gaze annotations for in-the-wild and outdoor scenarios.
Ranked #3 on
Gaze Estimation
on Gaze360
KAMA: 3D Keypoint Aware Body Mesh Articulation
We present KAMA, a 3D Keypoint Aware Mesh Articulation approach that allows us to estimate a human body mesh from the positions of 3D body keypoints.
Ranked #56 on
3D Human Pose Estimation
on 3DPW
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
We introduce DexYCB, a new dataset for capturing hand grasping of objects.
Learning to Track Instances without Video Annotations
Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation, and 2) the complexity of two-stage approaches.
Self-Supervised Viewpoint Learning From Image Collections
Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets.
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
Weakly Supervised 3D Hand Pose Estimation via Biomechanical Constraints
Estimating 3D hand pose from 2D images is a difficult, inverse problem due to the inherent scale and depth ambiguities.
Ranked #10 on
3D Hand Pose Estimation
on DexYCB
Weakly-Supervised 3D Human Pose Learning via Multi-view Images in the Wild
One major challenge for monocular 3D human pose estimation in-the-wild is the acquisition of training data that contains unconstrained images annotated with accurate 3D poses.
Monocular 3D Human Pose Estimation
Weakly-superavised 3D Human Pose Estimation
+1
A4 : Evading Learning-based Adblockers
Efforts by online ad publishers to circumvent traditional ad blockers towards regaining fiduciary benefits, have been demonstrably successful.
Few-Shot Adaptive Gaze Estimation
Inter-personal anatomical differences limit the accuracy of person-independent gaze estimation networks.
Ranked #1 on
Gaze Estimation
on MPII Gaze
(using extra training data)
AdGraph: A Graph-Based Approach to Ad and Tracker Blocking
AdGraph differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources.
Joint Flow: Temporal Flow Fields for Multi Person Tracking
The general formulation of our temporal network allows to rely on any multi person pose estimation approach as spatial network.
Hand Pose Estimation via Latent 2.5D Heatmap Regression
Estimating the 3D pose of a hand is an essential part of human-computer interaction.
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
In this work, we aim to further advance the state of the art by establishing "PoseTrack", a new large-scale benchmark for video-based human pose estimation and articulated tracking, and bringing together the community of researchers working on visual human analysis.
Ranked #3 on
Multi-Person Pose Estimation
on PoseTrack2017
A Dual-Source Approach for 3D Human Pose Estimation from a Single Image
To this end, we first convert the motion capture data into a normalized 2D pose space, and separately learn a 2D pose estimation model from the image data.
Ranked #38 on
Monocular 3D Human Pose Estimation
on Human3.6M
PoseTrack: Joint Multi-Person Pose Estimation and Tracking
In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrained videos.
Ranked #1 on
Pose Tracking
on Multi-Person PoseTrack
Multi-Person Pose Estimation
Multi-Person Pose Estimation and Tracking
+1
Multi-Person Pose Estimation with Local Joint-to-Person Associations
To this end, we consider multi-person pose estimation as a joint-to-person association problem.
Ranked #8 on
Multi-Person Pose Estimation
on MPII Multi-Person
Pose for Action - Action for Pose
In this work we propose to utilize information about human actions to improve pose estimation in monocular videos.
Ranked #5 on
Pose Estimation
on UPenn Action
A Dual-Source Approach for 3D Pose Estimation from a Single Image
To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient and robust 3D pose retrieval.
Ranked #24 on
3D Human Pose Estimation
on HumanEva-I
