Search Results for author:
Found 14 papers, 7 papers with code
Purposer: Putting Human Motion Generation in Context
We present a novel method to generate human motion to populate 3D indoor scenes.
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
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.
SHOWMe: Benchmarking Object-agnostic Hand-Object 3D Reconstruction
Recent hand-object interaction datasets show limited real object variability and rely on fitting the MANO parametric model to obtain groundtruth hand shapes.
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.
Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space
Learning causal relationships in high-dimensional data (images, videos) is a hard task, as they are often defined on low dimensional manifolds and must be extracted from complex signals dominated by appearance, lighting, textures and also spurious correlations in the data.
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 #59 on
3D Human Pose Estimation
on 3DPW
CoPhy: Counterfactual Learning of Physical Dynamics
Understanding causes and effects in mechanical systems is an essential component of reasoning in the physical world.
Learning Video Representations using Contrastive Bidirectional Transformer
This paper proposes a self-supervised learning approach for video features that results in significantly improved performance on downstream tasks (such as video classification, captioning and segmentation) compared to existing methods.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Object Level Visual Reasoning in Videos
Human activity recognition is typically addressed by detecting key concepts like global and local motion, features related to object classes present in the scene, as well as features related to the global context.
Ranked #1 on
Semantic Object Interaction Classification
on VLOG
Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
No spatial coherence is forced on the glimpse locations, which gives the module liberty to explore different points at each frame and better optimize the process of scrutinizing visual information.
Ranked #19 on
Skeleton Based Action Recognition
on N-UCLA
Human Action Recognition: Pose-based Attention draws focus to Hands
We propose a new spatio-temporal attention based mechanism for human action recognition able to automatically attend to the hands most involved into the studied action and detect the most discriminative moments in an action.
Pose-conditioned Spatio-Temporal Attention for Human Action Recognition
We show that it is of high interest to shift the attention to different hands at different time steps depending on the activity itself.
