#01Jul 23, 2026
cs.CV
Towards Robust Iris Recognition Through Occlusion Identification and Conditional Diffusion-Based Reconstruction
Kamrul Hasan, Mylene C. Q. Farias, Oleg V. Komogortsev
Iris recognition is a reliable biometric approach that identifies individuals using the distinctive and stable texture of the iris. However, recognition performance can degrade when discriminative iris texture is partially occluded by eyelids, eyelashes, specular reflections, or other acquisition artifacts. Existing approaches often perform recognition directly on degraded samples or rely only on the remaining visible iris region, which may be inadequate when substantial texture is corrupted. To address this limitation, we propose an occlusion-aware iris recognition framework with three sequential modules: occlusion-type identification, diffusion-based reconstruction, and deep-learning-based recognition. First, a residual 2D CNN-based network determines whether an iris image is non-occluded or belongs to one of the controlled occlusion categories. Second, the occluded image, binary mask, and predicted occlusion type condition a denoising diffusion probabilistic model to reconstruct the corrupted region. Finally, VGG19-HPMNet, a modified VGG19 model with horizontal pyramid mapping, extracts discriminative global and part-wise local iris features for recognition. Experiments on the CASIA-Iris-Thousand dataset under a controlled synthetic-occlusion protocol show that the proposed framework improves iris recognition performance by identifying the occlusion type, reconstructing masked regions, and re-evaluating the restored iris samples.
#02Jul 23, 2026
cs.CV
ASTRA-Net: Anatomy-Specific Transfer and Representation Alignment for Drug-Induced Sleep Endoscopy Segmentation
Suhua Sun, Yuqiao Wang, Sheng Liu and 6 more
Quantitative drug-induced sleep endoscopy (DISE) requires reliable airway boundaries at specific anatomical levels. Pixel-level DISE annotations are scarce, and manual contouring limits the scalability of quantitative assessment. To address this limitation, we developed ASTRA-Net for known-plane DISE segmentation with limited real annotations. Stage 1 aligned intermediate ConvNeXt-Base representations from 14,250 unlabeled virtual endoscopy frames derived from computed tomography and real DISE frames. Virtual images were used only for feature alignment. Stage 2 fine-tuned four independent UNet++ decoders on 401 real annotated frames. Structured zero-mask supervision constrained incompatible plane outputs and invalid frames. Six alignment configurations used maximum mean discrepancy, domain adversarial learning, or both objectives. On a hold-out evaluation set of 100 frames, the five-model MMD-only segmentation ensemble achieved a mean Dice of 0.8927, with a 95% image-level bootstrap interval of 0.8631 to 0.9160. The mean intersection over union was 0.8239. A classification- enabled variant of the same alignment configuration reached a restricted four-plane top-1 accuracy of 0.92 on the same hold-out frames. These results indicate that ASTRA-Net can support frame-level, plane-specific DISE boundary delineation when real annotations are limited.
#03Jul 23, 2026
cs.CV
Scene Parameter Saliency via Differentiable Light Transport
Linas Beresna, Eugene Fiume
Gradient-based saliency methods reveal which input features most influence a neural network's output, and are a standard tool for model interpretability. We observe that differentiable renderers, which are conventionally used for parameter optimisation, produce an analogous form of saliency: given any scalar metric evaluated on a rendered image, a single reverse-mode differentiation pass yields per-parameter gradients that identify which scene elements most influence the metric. We call these gradient fields metric saliency maps. Unlike neural saliency, which propagates attribution through learned weights, metric saliency propagates through the image formation process itself, including multi-bounce light transport, capturing parameter dependencies that are semi-opaque to manual inspection. We compute metric saliency maps for qualitatively different objectives: psychovisual glare indices, mean scene luminance, and neural perceptual scores. The saliency rankings differ substantially across metrics for the same scene, with parameters that dominate one objective being negligible for another. The saliency map is specific to the metric, not an intrinsic property of the scene. Our results suggest that differentiable renderers produce derivative images that are as informative for scene understanding as the primal images they were designed to generate.
#04Jul 23, 2026
cs.RO
GS-Agent: Creating 4D Physical Worlds With Generative Simulation
Hongxin Zhang, Chunru Lin, Junyan Li and 3 more
Creating dynamic and physically realistic 4D worlds from natural language descriptions is both fascinating and challenging. Traditional computer graphics methods rely on manual creation, requiring extensive human effort to fine-tune materials, motions, and visual fidelity. Recent advances in generative foundation models have sparked interest in learning to generate such 4D worlds from large-scale data; however, existing methods still struggle to ensure physical plausibility and controllability. In this work, we take a different path by leveraging foundation models to construct an agentic system that emulates how humans traditionally create 4D worlds, yet automates the entire process. We present GS-Agent, an end-to-end multi-agent framework that integrates physics engines in the loop to generate realistic, dynamic, and controllable 4D physical worlds from natural language. Inspired by how humans build 4D worlds, GS-Agent decomposes the task into entity management, covering 3D asset curation, material tuning, placement, and motion control, and rendering configuration, including camera and lighting manipulation. Multiple agents with distinct expertise interact with the physics engine via code, seek multimodal feedback, and collaborate to iteratively construct 4D worlds that align with the given descriptions. Experimental results show that GS-Agent effectively converts natural language into diverse and physically plausible 4D worlds exhibiting rich interactions among liquids, deformable objects, and rigid bodies, while achieving cinematic camera and lighting control. We envision GS-Agent as a foundation for a new paradigm in 4D world generation, empowering creative content creation and physical AI. Project page at https://umass-embodied-agi.github.io/gs-agent/
#05Jul 23, 2026
cs.CV
Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation
Hyunmin Cho, Jaejun Yoo, Kyong Hwan Jin
We study sinusoidal recurrence as an iterative mechanism for harmonic spectral enrichment in implicit neural representations (INRs). Our analysis reveals that sinusoidal activations induce a harmonic line spectrum, providing a spectral account of how recurrent unrolling enriches the effective spectral support. We realize this principle with a shared sinusoidal block that iteratively refines the latent representation. We empirically validate the resulting spectral behavior against feed-forward INRs, non-sinusoidal recurrent variants, and equilibrium-style sinusoidal models. Complementing this analysis, we evaluate the proposed architecture across image and 3D representation tasks. On RGB image benchmarks, our method achieves higher fidelity than feed-forward baselines with fewer parameters and fewer optimization steps, and it further transfers favorably to super-resolution, NeRF, and SDF tasks.