5 papers
PoseDreamer: Scalable and Photorealistic Human Data Generation Pipeline with Diffusion Models
Lorenza Prospero, Orest Kupyn, Ostap Viniavskyi +2
Acquiring labeled datasets for 3D human mesh estimation is challenging due to depth ambiguities and the inherent difficulty of annotating 3D geometry from monocular images. Existin…
On the Dangers of Bootstrapping Generation for Continual Learning and Beyond
Daniil Zverev, A. Sophia Koepke, Joao F. Henriques
The use of synthetically generated data for training models is becoming a common practice. While generated data can augment the training data, repeated training on synthetic data r…
Interpretable Representation Learning from Videos using Nonlinear Priors
Marian Longa, João F. Henriques
Learning interpretable representations of visual data is an important challenge, to make machines' decisions understandable to humans and to improve generalisation outside of the t…
GST: Precise 3D Human Body from a Single Image with Gaussian Splatting Transformers
Lorenza Prospero, Abdullah Hamdi, Joao F. Henriques +1
Reconstructing posed 3D human models from monocular images has important applications in the sports industry, including performance tracking, injury prevention and virtual training…
Unsupervised Object Detection with Theoretical Guarantees
Marian Longa, João F. Henriques
Unsupervised object detection using deep neural networks is typically a difficult problem with few to no guarantees about the learned representation. In this work we present the fi…