Generating human motion guided by conditions such as textual descriptions is challenging due to the need for datasets with pairs of high-quality motion and their corresponding conditions. The difficulty increases when aiming for finer control in the generation. To that end, prior works have proposed to combine several motion diffusion models pre-trained on datasets with different types of conditions, thus allowing control with multiple conditions. However, the proposed merging strategies overlook that the optimal way to combine the generation processes might depend on the particularities of each pre-trained generative model and also the specific textual descriptions. In this context, we introduce MixerMDM, the first learnable model composition technique for combining pre-trained text-conditioned human motion diffusion models. Unlike previous approaches, MixerMDM provides a dynamic mixing strategy that is trained in an adversarial fashion to learn to combine the denoising process of each model depending on the set of conditions driving the generation. By using MixerMDM to combine single- and multi-person motion diffusion models, we achieve fine-grained control on the dynamics of every person individually, and also on the overall interaction. Furthermore, we propose a new evaluation technique that, for the first time in this task, measures the interaction and individual quality by computing the alignment between the mixed generated motions and their conditions as well as the capabilities of MixerMDM to adapt the mixing throughout the denoising process depending on the motions to mix.

Human action recognition is a widely explored problem in computer vision, with applications in domains such as robotics and surveillance. While most existing methods focus on predicting a single action class, human actions are not isolated events but range from simple movements to complex behaviors. Recent work has approached action recognition from a hierarchical perspective, relying on manually annotated labels available in some domains. In contrast, the Activities of Daily Living (ADL) domain is constrained by a lack of structured datasets for this purpose. In this work, we introduce HierADL, a framework for automatically generating action hierarchies in the ADL domain. HierADL leverages semantic features of action labels and visual features from video clips to group fine-grained actions into coarse-grained categories. These hierarchies are integrated with the HierADL classifier, which allows simultaneous prediction of both fine-grained and coarse-grained actions to improve accuracy, besides being compatible with most video classification architectures. In addition, we conduct an ablation study to identify the most effective method for generating action hierarchies based on visual and semantic cues. We evaluate HierADL hierarchies qualitatively using t-SNE plots, and quantitatively on two ADL datasets: ETRI-Activity and Hierarchical TSU. Our results demonstrate that HierADL outperforms fine-grained-only approaches on several state-of-the-art video backbones, achieving an accuracy improvement of over 3.5%.

Reconstructing high-quality 3D textured models from single images of hand-held objects is a challenging task due to occlusions, complex interactions between hands and objects, and the need for precise segmentation and texture reconstruction. In this paper, we introduce Obj3Dify, a novel framework that addresses these challenges by integrating state-of-the-art techniques in computer vision, generative modeling, and 3D reconstruction. The pipeline includes object detection and classification with LLaVA-NeXT, segmentation with Lang-SAM, occlusion inpainting with Stable Diffusion XL, and 3D model generation using TRELLIS.We evaluate our framework on the HO3D dataset, leveraging its comprehensive 3D annotations and diverse object shapes for robust benchmarking. Comparative analyses with In-Hand3D and TRELLIS demonstrate that Obj3Dify significantly improves geometric fidelity and reduces noise in 3D reconstructions, achieving results closer to ground-truth models. An ablation study further validates the contributions of each pipeline stage, highlighting the critical role of segmentation and occlusion inpainting in enhancing 3D model quality. Furthermore, a multiview approach is implemented and tested, demonstrating its usefulness for generating asymmetric and organic figures.Our results establish Obj3Dify as an effective solution for occlusion-free 3D object reconstruction, advancing 3D modeling for real-world hand-object scenarios with applications in AR, robotics, and virtual content creation.

This paper proposes a multimodal deep learning architecture combining text and audio information to predict dementia, a disease which affects around 55 million people all over the world and makes them in some cases dependent people. The system was evaluated on the DementiaBank Pitt Corpus dataset, which includes audio recordings as well as their transcriptions for healthy people and people with dementia. Different models have been used and tested, including Convolutional Neural Networks (CNN) for audio classification, Transformers for text classification, and a combination of both in a multimodal ensemble. These models have been evaluated on a test set, obtaining the best results by using the text modality, achieving 90.36% accuracy on the task of detecting dementia. Additionally, an analysis of the corpus has been conducted for the sake of explainability, aiming to obtain more information about how the models generate their predictions and identify patterns in the data.