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Found 17 papers, 7 papers with code
MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild
Here, we propose a solution to generate a large set of photorealistic synthetic images of humans with 3D pose annotations.
Ranked #117 on
3D Human Pose Estimation
on Human3.6M
(PA-MPJPE metric)
Detecting Parts for Action Localization
In this paper, we propose a new framework for action localization that tracks people in videos and extracts full-body human tubes, i. e., spatio-temporal regions localizing actions, even in the case of occlusions or truncations.
Image-based Synthesis for Deep 3D Human Pose Estimation
Here, we propose a solution to generate a large set of photorealistic synthetic images of humans with 3D pose annotations.
Mimetics: Towards Understanding Human Actions Out of Context
Our experiments show that (a) state-of-the-art 3D convolutional neural networks obtain disappointing results on such videos, highlighting the lack of true understanding of the human actions and (b) models leveraging body language via human pose are less prone to context biases.
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.
DOPE: Distillation Of Part Experts for whole-body 3D pose estimation in the wild
We introduce DOPE, the first method to detect and estimate whole-body 3D human poses, including bodies, hands and faces, in the wild.
SMPLy Benchmarking 3D Human Pose Estimation in the Wild
Predicting 3D human pose from images has seen great recent improvements.
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning
In this context, we show that one way to learn models that are inherently more robust against forgetting is domain randomization - for vision tasks, randomizing the current domain's distribution with heavy image manipulations.
Towards fast and effective single-step adversarial training
In this work, we methodically revisit the role of noise and clipping in single-step adversarial training.
Leveraging MoCap Data for Human Mesh Recovery
In fact, we show that simply fine-tuning the batch normalization layers of the model is enough to achieve large gains.
Ranked #60 on
3D Human Pose Estimation
on 3DPW
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
Recently, Wong et al. showed that adversarial training with single-step FGSM leads to a characteristic failure mode named Catastrophic Overfitting (CO), in which a model becomes suddenly vulnerable to multi-step attacks.
PoseBERT: A Generic Transformer Module for Temporal 3D Human Modeling
It is simple, generic and versatile, as it can be plugged on top of any image-based model to transform it in a video-based model leveraging temporal information.
PoseGPT: Quantization-based 3D Human Motion Generation and Forecasting
The discrete and compressed nature of the latent space allows the GPT-like model to focus on long-range signal, as it removes low-level redundancy in the input signal.
PoseScript: Linking 3D Human Poses and Natural Language
Thirdly, we present a learned process for generating pose descriptions.
PoseFix: Correcting 3D Human Poses with Natural Language
Automatically producing instructions to modify one's posture could open the door to endless applications, such as personalized coaching and in-home physical therapy.
Cross-view and Cross-pose Completion for 3D Human Understanding
Human perception and understanding is a major domain of computer vision which, like many other vision subdomains recently, stands to gain from the use of large models pre-trained on large datasets.
Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot
We present Multi-HMR, a strong single-shot model for multi-person 3D human mesh recovery from a single RGB image.
Ranked #1 on
3D Human Pose Estimation
on UBody
