Effective training of face recognition models requires large-scale datasets of facial identities, yet collecting suitable data is time-consuming and raises privacy concerns. Existing deep generative models offer a promising alternative through the synthesis of high-quality images but often fail to fully preserve identity information. In this work, we propose IDSync, a novel generative diffusion-based framework designed to produce synthetic face images with more consistent identities that are better suited for training recognition models. To this end, IDSync employs a denoising network in the latent space of a frozen variational autoencoder, with identity guidance introduced via a text encoder that interprets identity embeddings from a pretrained recognition model. During training, the framework leverages a pretrained auxiliary identity classifier to define an additional cross-entropy loss, which is backpropagated to improve identity consistency. We evaluate the generated images using inter- and intra-class cosine similarity of identity features along with a variety of statistical measures between synthetic and real distributions focused on fidelity and diversity. To assess utility, we train face recognition models on the synthetic images and measure accuracy on standard verification benchmarks. Experimental results show that recognition models trained on IDSync-generated data achieve higher verification accuracies on real-world benchmarks than models trained on synthetic data produced by competing generative models. The IDSync source code is publicly available at https://github. com/JSabadin/IDSync.
As robots increasingly operate in human-populated environments, anticipating human intentions is essential for enabling proactive and socially aware behavior. Automatic anticipation of human-robot interactions is thus emerging as a crucial perception challenge for embodied agents. To this end, we introduce HUI360, the largest dataset for human-robot interaction anticipation in the wild and its set of baselines. The dataset was collected from a mobile robot, in the wild, over multiple days within a 3-month period, and in several environments, capturing natural, spontaneous behaviors from both passersby and users, and encompassing a diverse range of individuals. This variety enables evaluating and improving the generalization capabilities of interaction anticipation models. We designed a pipeline and share code for automatic interaction annotation in arbitrary 360-degree equirectangular videos, along with interfaces for manual refinement. Using this pipeline, we release the HUI360 open set of 1M pre-processed annotations, including detailed 2D poses, facial keypoints, and segmentation masks, obtained using state-of-the-art computer vision methods and manually curated to ensure high-quality tracking and interaction annotation. Additionally, we release the raw panoptic 360-degree images captured from the robot's egocentric viewpoint (on demand, for research purpose only in compliance with GDPR). Finally, we establish benchmark baselines for interaction anticipation, including the first cross-dataset evaluations for this task: to this end, we also release 6M annotations for another existing in-the-wild outdoor dataset collected from a mobile robot (SSUP-HRI). Dataset and code can be found at https://hucebot.github.io/hui360.
Privacy regulations (e.g., GDPR, CCPA) mandate that public datasets with permissive commercial licenses (e.g., CC BY 4.0) containing humans be pseudonymized before use. However, existing anonymization methods have notable limitations: blurring or masking degrade downstream utility, GAN-based synthesis offers limited control and photorealism, and diffusion editors may retain identity traces. To overcome these limitations, we propose Rendering-Refined Stable Diffusion (RefSD), a three-stage pipeline that (1) removes real humans via segmentation and inpainting, (2) reconstructs pose-aligned, identity-free avatars through SMPL-based 3D rendering, and (3) refines appearance with text-guided diffusion for photorealism. By using rendering, RefSD provides explicit control over body shape, clothing, and pose, enabling diverse yet structured avatar generation. To validate human alignment, we introduce HumanGenAI, a human-annotation suite for evaluating privacy preservation, perceptual satisfaction, and attribute-generation fidelity. Beyond HumanGenAI, we conduct re-identification and downstream task benchmarks, demonstrating that RefSD matches the re-ID performance of complete masking while achieving competitive utility relative to real images. Together, RefSD and HumanGenAI establish a scalable pipeline and benchmark for privacy-compliant human synthesis in image datasets. Project: https:// rendering-refined-stable-diffusion.github.io/
Wi-Fi sensing technology has advanced to the point where it can reconstruct 3D human meshes, offering a low-cost and privacy-preserving paradigm for human action recognition. However, existing methods for processing these meshes typically rely on dense vertices spread across the entire body to maintain recognition accuracy. This approach imposes a heavy computational burden, making it difficult to deploy in resource-constrained WiFi edge devices. Our work presents an in-depth analysis of vertex distribution in these approaches. We find that the informational value of vertices is not uniformly distributed across the body. Based on this, we propose the ARiSE framework, which introduces a two-stage optimization strategy. First, we address the inherent complexity of the human anatomy. We recognize that body parts with fine motor details, like the hands and torsos, require a higher density of vertices to be accurately represented. Our selective down-sampling method is therefore designed to strategically preserve more vertices in these critical regions while reducing them elsewhere. Second, we introduce a dynamic, motion-aware weighting mechanism. For any given action, different body parts contribute unequally to the movement energy. Our framework learns to identify the high-energy parts and focus more on them during recognition. We proposes a energy-based mixture of experts (MoE) in which each body part is processed by a lightweight CNN expert. A learned motion-aware gate estimates part energy over time and sets the expert weights so inference focuses on the parts that drive the action. This dual approach ensures that we not only start with a more strategic, anatomy-aware distribution of points, but also dynamically focus on the most expressive body parts as an action unfolds. Extensive experiments validate our approach. Critically, we are the first to demonstrate that ARiSE achieves state-of-the-art accuracy on resourceconstrained edge platforms like the NVIDIA Jetson TX2, while reducing computational overhead by more than an order of magnitude. This confirms the practical feasibility of on-device mesh-based action recognition with Wi-Fi sensing.
Skeleton-based gait analysis commonly relies on fixed, hand-crafted spatial-temporal graphs derived from human anatomy, which are often sub-optimal for specific datasets and tasks and can exacerbate over-smoothing in deep graph neural networks. This paper proposes a novel method to automatically simplify the spatial-temporal graph representation for skeleton-based gait analysis, improving interpretability without losing performance and aiding optimization by alleviating over-smoothing in message-passing graph networks. Our approach employs two models, an upstream model that can estimate a per-instance spatial-temporal adjacency matrix for a skeleton sequence, thereby removing the fixed nature of the spatial-temporal graph, and a downstream model that processes the sequence with the updated adjacency matrix. Even though the estimation of the updated adjacency matrix is nondifferentiable, our model is trainable end-to-end by employing the Straight-Through Gumbel-Softmax trick for gradient estimation. Experiments on the CASIA-B dataset for gait-based gender classification show that the learned simplified graphs are more interpretable, significantly reduce over-smoothing in deeper models, and achieve performance that is on par with or superior to fixed-graph baselines while using substantially fewer connections. These results demonstrate that task-adaptive graph simplification offers a principled path toward more robust, interpretable and efficient gait-based biometric systems.
