1 citations · 3 across the 8 of their papers we have counts for
8 papers · 1 filter
COSMU: Complete 3D human shape from monocular unconstrained images
Marco Pesavento, Marco Volino, Adrian Hilton
We present a novel framework to reconstruct complete 3D human shapes from a given target image by leveraging monocular unconstrained images. The objective of this work is to reprod…
Benchmarking Monocular 3D Dog Pose Estimation Using In-The-Wild Motion Capture Data
Moira Shooter, Charles Malleson, Adrian Hilton
We introduce a new benchmark analysis focusing on 3D canine pose estimation from monocular in-the-wild images. A multi-modal dataset 3DDogs-Lab was captured indoors, featuring vari…
An Effective-Efficient Approach for Dense Multi-Label Action Detection
Faegheh Sardari, Armin Mustafa, Philip J. B. Jackson +1
Unlike the sparse label action detection task, where a single action occurs in each timestamp of a video, in a dense multi-label scenario, actions can overlap. To address this chal…
ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image
Marco Pesavento, Yuanlu Xu, Nikolaos Sarafianos +7
Recent progress in human shape learning, shows that neural implicit models are effective in generating 3D human surfaces from limited number of views, and even from a single RGB im…
CAD -- Contextual Multi-modal Alignment for Dynamic AVQA
Asmar Nadeem, Adrian Hilton, Robert Dawes +2
In the context of Audio Visual Question Answering (AVQA) tasks, the audio visual modalities could be learnt on three levels: 1) Spatial, 2) Temporal, and 3) Semantic. Existing AVQA…
PAT: Position-Aware Transformer for Dense Multi-Label Action Detection
Faegheh Sardari, Armin Mustafa, Philip J. B. Jackson +1
We present PAT, a transformer-based network that learns complex temporal co-occurrence action dependencies in a video by exploiting multi-scale temporal features. In existing metho…