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Found 14 papers, 6 papers with code
PoseScript: 3D Human Poses from Natural Language
This process extracts low-level pose information -- the posecodes -- using a set of simple but generic rules on the 3D keypoints.
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.
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.
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.
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 #3 on
3D Human Pose Estimation
on MPI-INF-3DHP
(Acceleration Error metric)
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.
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.
SMPLy Benchmarking 3D Human Pose Estimation in the Wild
Predicting 3D human pose from images has seen great recent improvements.
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.
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.
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.
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.
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.
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 #94 on
3D Human Pose Estimation
on Human3.6M
(PA-MPJPE metric)
