Despite significant advancements in text-to-motion syn-thesis, generating language-guided human motion within 3D environments poses substantial challenges. These challenges stem primarily from (i) the absence of powerful generative models capable of jointly modeling natural language, 3D scenes, and human motion, and (ii) the generative models' in-tensive data requirements contrasted with the scarcity of comprehensive, high-quality, language-scene-motion datasets. To tackle these issues, we introduce a novel two-stage frame-work that employs scene affordance as an intermediate representation, effectively linking 3D scene grounding and conditional motion generation. Our framework comprises an Affordance Diffusion Model (ADM) for predicting ex-plicit affordance map and an Affordance-to-Motion Diffusion Model (AMDM) for generating plausible human motions. By leveraging scene affordance maps, our method overcomes the difficulty in generating human motion under multimodal condition signals, especially when training with limited data lacking extensive language-scene-motion pairs. Our exten-sive experiments demonstrate that our approach consistently outperforms all baselines on established benchmarks, in-cluding HumanML3D and HUMANISE. Additionally, we validate our model's exceptional generalization capabilities on a specially curated evaluation set featuring previously unseen descriptions and scenes.

The proliferation of online dating platforms has provided new opportunities for deaf and hard of hearing (DHH) individuals to seek, build, and maintain romantic relationships. However, challenges such as deception, harassment, and aggression widely exist, closely tied to their practices of self-presentation and disability disclosure. Yet, these practices of DHH individuals in online dating communities remain largely unknown. Our study presents a preliminary qualitative analysis of 1,200 posts from a leading DHH online dating community. Our findings reveal that DHH individuals hesitate to reveal their disability while actively disclosing their relationship challenges, especially with respect to hearing-DHH relationships. We have also identified significant research gaps in understanding online DHH dating activities, calling for future research to make online dating environments more inclusive for DHH individuals.

Network simulators are an essential tool for network operators, and can assist important tasks such as capacity planning, topology design, and parameter tuning. Popular simulators are all based on discrete event simulation, and their performance does not scale with the size of modern networks. Recently, deep-learning-based techniques are introduced to solve the scalability problem, but, as we show with experiments, they have poor visibility in their simulation results, and cannot generalize to diverse scenarios. In this work, we combine scalable and generalized continuous simulation techniques with discrete event simulation to achieve high scalability, while providing packet-level visibility. We start from a solid queueing-theoretic modeling of modern networks, and carefully identify the mathematically-intractable or computationally-expensive parts, only which are then modeled using deep neural networks (DNN). Dubbed DeepQueueNet, our approach combines prior knowledge of networks, and supports arbitrary topology and device traffic management mechanisms (given sufficient training data). Our extensive experiments show that DeepQueueNet achieves near-linear speedup in the number of GPUs, and its estimation accuracy for average and 99th percentile round-trip time outperforms existing end-to-end DNN-based performance estimators in all scenarios.

Recently, numerous perceptual video coding approaches have been proposed to use face as ROI regions, for improving perceived visual quality of compressed conversational videos. However, there exists no objective metric, specialized for efficiently evaluating the perceived visual quality of compressed conversational videos. This paper thus proposes an efficient objective quality assessment method, namely Gaussian mixture model based PSNR (GMM-PSNR), for conversational videos. First, eye tracking experiments, together with a face extraction technique, were carried out to identify importance of the regions of background, face, and facial features, through eye fixation points. Next, assuming that the distribution of some eye fixation points obeys Gaussian mixture model, an importance weight map is generated by introducing a new term, eye fixation points/pixel(efp/p). Finally, GMM-PSNR is computed by assigning different penalties to the distortion of each pixel in a video frame, according to the generated weight map. The experimental results show the effectiveness of our GMM-PSNR by investigating its correlation with subjective quality on several test video sequences.