3D semantic scene completion is critical for multiple downstream tasks in autonomous systems. It estimates missing geometric and semantic information in the acquired scene data. Due to the challenging real-world conditions, this task usually demands complex models that process multi-modal data to achieve acceptable performance. We propose a unique neural model, leveraging advances from the state space and diffusion generative modeling to achieve remarkable 3D semantic scene completion performance with monocular image input. Our technique processes the data in the conditioned latent space of a variational autoencoder where diffusion modeling is carried out with an innovative state space technique. A key component of our neural network is the proposed Skimba (Skip Mamba) denoiser, which is adept at efficiently processing long-sequence data. The Skimba diffusion model is integral to our 3D scene completion network, incorporating a triple Mamba structure, dimensional decomposition residuals and varying dilations along three directions. We also adopt a variant of this network for the subsequent semantic segmentation stage of our method. Extensive evaluation on the standard SemanticKITTI and SSCBench-KITTI360 datasets show that our approach not only outperforms other monocular techniques by a large margin, it also achieves competitive performance against stereo methods.

Outdoor 3D semantic scene generation produces realistic and semantically rich environments for applications such as urban simulation and autonomous driving. However, advances in this direction are constrained by the absence of publicly available, well-annotated datasets. We introduce SketchSem3D, the first large‑scale benchmark for generating 3D outdoor semantic scenes from abstract freehand sketches and pseudo‑labeled annotations of satellite images. SketchSem3D includes two subsets, Sketch-based SemanticKITTI and Sketch-based KITTI-360 (containing LiDAR voxels along with their corresponding sketches and annotated satellite images), to enable standardized, rigorous, and diverse evaluations. We also propose Cylinder Mamba Diffusion (CymbaDiff) that significantly enhances spatial coherence in outdoor 3D scene generation. CymbaDiff imposes structured spatial ordering, explicitly captures cylindrical continuity and vertical hierarchy, and preserves both physical neighborhood relationships and global context within the generated scenes. Extensive experiments on SketchSem3D demonstrate that CymbaDiff achieves superior semantic consistency, spatial realism, and cross-dataset generalization. The code and dataset will be available at here.

Bias in text-to-image (T2I) generation can propagate unfair social representations and may be exploited to push ulterior agendas. These biases raise concerns on the dependability and fairness of models that have become widely popular and readily available for public consumption. Existing works in T2I bias analysis typically focus on social biases. We look beyond that and instead propose an evaluation methodology to quantify general bias in T2I generative models without any preconceived notion. We introduce a suite of three metrics; namely, distribution bias, Jaccard hallucination and generative miss-rate, to extensively appraise general model bias. To validate the efficacy of these metrics, we also introduce a backdoor-inspired strategy, which provides a convenient handle over the extent of bias in a model for controlled analysis. We assess T2I models implementing six widely used pipelines in this domain. Our extensive analysis covers both general and task-oriented scenarios, employing over 105 K generated images. For prior art comparison, it also encompasses social bias analysis. Moreover, we also extend our technique to analyze bias in seven popular captioned image datasets. Our experiments establish that our approach is objective, domain-agnostic and it consistently measures different forms of T2I model biases. To further research efforts into T2I model biases, we have developed an open-source web application and practical implementation of this work, which is available onhttps://huggingface.co/spaces/JVice/try-before-you-biasHuggingFace. All relevant code is also publicly available onhttps://github.com/JJ-Vice/TryBeforeYouBiasGitHub.

Recent advances in generative visual content have led to a quantum leap in the quality of artificially generated Deepfake content. Especially, diffusion models are causing growing concerns among communities due to their ever-increasing realism. However, quantifying the realism of generated content is still challenging. Existing evaluation metrics, such as Inception Score and Fréchet inception distance, fall short on benchmarking diffusion models due to the versatility of the generated images. To address this, we propose the Image Realism Score (IRS) evaluation metric, computed from five statistical measures of a given image. This non-learning-based metric not only efficiently quantifies the realism of generated images, but it is also a viable tool for detecting if an image is real or fake. We experimentally establish the model- and data-agnostic nature of the proposed IRS by successfully detecting fake images generated by Stable Diffusion Model (SDM), Dalle2, Dalle3, Deepfloyd, Kandinsky, Midjourney and BigGAN.

Macrolevel facial muscle variations, as used for building models of seven discrete facial expressions, suffice when distinguishing between macrolevel human affective states but won’t discretise continuous and dynamic microlevel variations in facial expressions. We present a hierarchical separation and classification network (HSCN) for discovering dynamic, continuous, and macro- and microlevel variations in facial expressions of affective states. In the HSCN, we first invoke an unsupervised cosine similarity-based separation method on continuous facial expression data to extract twenty-one dynamic facial expression classes from the seven common discrete affective states. The between-clusters separation is then optimized for discovering the macrolevel changes resulting from facial muscle activations. A following step in the HSCN separates the upper and lower facial regions for realizing changes pertaining to upper and lower facial muscle activations. Data from the two separated facial regions are then clustered in a linear discriminant space using similarities in muscular activation patterns. Next, the actual dynamic expression data are mapped onto discriminant features for developing a rule-based expert system that facilitates classifying twenty-one upper and twenty-one lower microexpressions. Invoking the random forest algorithm would classify twenty-one macrolevel facial expressions with 76.11% accuracy. A support vector machine (SVM), used separately on upper and lower facial regions in tandem, could classify them with respective accuracies of 73.63% and 87.68%. This work demonstrates a novel and effective method of dynamic assessment of affective states. The HSCN further demonstrates that facial muscle variations gathered from either upper, lower, or full-face would suffice classifying affective states. We also provide new insight into discovery of microlevel facial muscle variations and their utilization in dynamic assessment of facial expressions of affective states.

The rise in popularity of text-to-image generative artificial intelligence (AI) has attracted widespread public interest. We demonstrate that this technology can be attacked to generate content that subtly manipulates its users. We propose a Backdoor Attack on text-to-image Generative Models (BAGM), which upon triggering, infuses the generated images with manipulative details that are naturally blended in the content. Our attack is the first to target three popular text-to-image generative models across three stages of the generative process by modifying the behaviour of the embedded tokenizer, the language model or the image generative model. Based on the penetration level, BAGM takes the form of a suite of attacks that are referred to assurface,shallowanddeepattacks in this article. Given the existing gap within this domain, we also contribute a comprehensive set of quantitative metrics designed specifically for assessing the effectiveness of backdoor attacks on text-to-image models. The efficacy of BAGM is established by attacking state-of-the-art generative models, using a marketing scenario as the target domain. To that end, we contribute a dataset of branded product images. Our embedded backdoors increase the bias towards the target outputs by more than five times the usual, without compromising the model robustness or the generated content utility. By exposing generative AI’s vulnerabilities, we encourage researchers to tackle these challenges and practitioners to exercise caution when using pre-trained models. Relevant code and input prompts can be found at https://github.com/JJ-Vice/BAGM, and the dataset is available at: https://ieee-dataport.org/documents/marketable-foods-mf-dataset.