Examinando por Autor "Comino-Trinidad, Marc"

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Accurate hand contact detection from RGB images via image-to-image translation
(Elsevier, 2025-05) Sorli, Suzanne; Comino-Trinidad, Marc; Casas, Dan
Hand tracking is a growing research field that can potentially provide a natural interface to interact with virtual environments. However, despite the impressive recent advances, the 3D tracking of two interacting hands from RGB video remains an open problem. While current methods are able to infer the 3D pose of two hands in interaction reasonably, residual errors in depth, shape, and pose estimation prevent the accurate detection of hand-to-hand contact. To mitigate these errors, in this paper, we propose an image-based data-driven method to estimate the contact in hand-to-hand interactions. Our method is built on top of 3D hand trackers that predict the articulated pose of two hands, enriching them with camera-space probability maps of contact points. To train our method, we first feed motion capture data of interacting hands into a physics-based hand simulator, and compute dense 3D contact points. We then render such contact maps from various viewpoints and create a dataset of pairs of pixel-to-surface hand images and their corresponding contact labels. Finally, we train an image-to-image network that learns to translate pixel-to-surface correspondences to contact maps. At inference time, we estimate pixel-to-surface correspondences using state-of-the-art hand tracking and then use our network to predict accurate hand-to-hand contact. We qualitatively and quantitatively validate our method in real-world data and demonstrate that our contact predictions are more accurate than state-of-the-art hand-tracking methods.
SMPLitex: A Generative Model and Dataset for 3D Human Texture Estimation from Single Image
(British Machine Vision Association, 2023) Casas , Dan; Comino-Trinidad, Marc
We propose SMPLitex, a method for estimating and manipulating the complete 3D appearance of humans captured from a single image. SMPLitex builds upon the recently proposed generative models for 2D images, and extends their use to the 3D domain through pixel-to-surface correspondences computed on the input image. To this end, we first train a generative model for complete 3D human appearance, and then fit it into the input image by conditioning the generative model to the visible parts of subject. Furthermore, we propose a new dataset of high-quality human textures built by sampling SMPLitex conditioned on subject descriptions and images. We quantitatively and qualitatively evaluate our method in 3 publicly available datasets, demonstrating that SMPLitex significantly outperforms existing methods for human texture estimation while allowing for a wider variety of tasks such as editing, synthesis, and manipulation.