SOMA: From Surface Observations to Muscle Anatomy

With the growing demand for realistic virtual humans, parametric body models have become a cornerstone of modern medicine, sports, and entertainment applications. However, most of these models are inherently limited: they only capture the 3D surface of the skin, offering no insight into the complex bio-mechanical structures that generate motion. As more applications expand towards biomechanics, the need for virtual human models that go beyond the skin has become increasingly evident. Traditional soft-tissue simulations, such as FEM, are accurate but non-scalable and too computationally expensive for most common applications. Alternatively, existing biomechanical tools can simulate muscular forces and activations, but do not model changes in external shape, restricting how activations correlate with actual observable anatomy. This motivates a novel inverse research problem: recovering muscle deformations directly from visible surface observations - i.e., from the skin, and thus the pose. In this work, we present SOMA (from Surface Observations to Muscle Anatomy), a person-specific model that infers spatio-temporal muscle behavior from surface signals obtained using RGB cameras, and SKIM, a subject-specific soft-tissue deformation dataset. To the best of our knowledge, this is the first method that attempts to recover muscle deformations from multi-view RGB data. We show how our method provides anatomically grounded animations without the complexity of traditional simulations, leading to a scalable and cost-effective solution. Data and code are available.

Curriculum-Based Strategies for Efficient Cross-Domain Action Recognition

Despite significant progress in human action recognition, generalizing to diverse viewpoints remains a challenge. Most existing datasets are captured from ground-level perspectives, and models trained on them often struggle to transfer to drastically different domains such as aerial views. This paper examines how curriculum-based training strategies can improve generalization to unseen real aerial-view data without using any real aerial data during training. We explore curriculum learning for cross-view action recognition using two out-of-domain sources: synthetic aerial-view data and real ground-view data. Our results on the evaluation on order of training (fine-tuning on synthetic aerial data vs. real ground data) shows that fine-tuning on real ground data but differ in how they transition from synthetic to real. The first uses a two-stage curriculum with direct fine-tuning, while the second applies a progressive curriculum that expands the dataset in multiple stages before fine-tuning. We evaluate both methods on the REMAG dataset using SlowFast (CNN-based) and MViTv2 (Transformer-based) architectures. Results show that combining the two out-of-domain datasets clearly outperforms training on a single domain, whether real ground-view or synthetic aerial-view. Both curriculum strategies match the top-1 accuracy of simple dataset combination while offering efficiency gains. With the two-step fine-tuning method, SlowFast achieves up to a 37% reduction in iterations and MViTv2 up to a 30% reduction compared to simple combination. The multi-step progressive approach further reduces iterations, by up to 9% for SlowFast and 30% for MViTv2, relative to the two-step method. These findings demonstrate that curriculum-based training can maintain comparable performance (top-1 accuracy within 3% range) while improving training efficiency in cross-view action recognition.

Texture- and Shape-based Adversarial Attacks for Vehicle Detection in Synthetic Overhead Imagery

Detecting vehicles in aerial images can be very challenging due to complex backgrounds, small resolution, shadows, and occlusions. Despite the effectiveness of SOTA detectors such as YOLO, they remain vulnerable to adversarial attacks (AAs), compromising their reliability. Traditional AA strategies often overlook the practical constraints of physical implementation, focusing solely on attack performance. Our work addresses this issue by proposing practical implementation constraints for AA in texture and/or shape. These constraints include pixelation, masking, limiting the color palette of the textures, and constraining the shape modifications. We evaluated the proposed constraints through extensive experiments using three widely used object detector architectures, and compared them to previous works. The results demonstrate the effectiveness of our solutions and reveal a trade-off between practicality and performance. Additionally, we introduce a labeled dataset of overhead images featuring vehicles of various categories. We will make the code/dataset public upon paper acceptance.