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Yihua Shao

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Preprint Sep 2026

Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction

4D scene reconstruction aims to recover the evolving geometry, appearance, and motion of dynamic environments from visual observations. Despite substantial progress in neural scene representations, reconstructing dynamic scenes remains challenging due to non-rigid motion, occlusions, temporal inconsistencies, and the t...

Zi-Ren Gong, Guo Chen, Yong-Jian Li et al. · 0 citations
Review Aug 2026

Human-Centric Intelligence in the Era of Foundation Models: A Survey

A full-spectrum human context taxonomy is introduced that integrates six interconnected levels by viewing humans as observable subjects through visual appearance and spatial geometry, as dynamic actors through kinematic dynamics and interaction modeling, and as situated agents through world simulation and embodied agen...

Yang Chen, Tianqi Wang, Xiao-Wen Jiang et al. · 0 citations
Preprint Aug 2026

HUG-VIS: A Multimodal Benchmark for Human-centered Understanding and Generation in Visual Intelligence

HUG-VIS, a unified benchmark for Human-centered Understanding and Generation in Visual Intelligence, contains 8,400 seated half-body videos of 30 professional actors, each performing the same 280 emotion-action-prompt assignments under a controlled Mandarin studio protocol, with synchronized video, audio, text, and alp...

Fei Ma, Ze-Bang Cheng, Ming-Hui Li et al. · 0 citations
Conference Open access Sep 2026

Gradient Enhancement Task Aware Post-training Quantization

This paper introduces Gradient Enhancement Task Aware Post-training Quantization, i.e., GTAQ, to address the generalization issue of Large Language Models, and extensively evaluates the LLaMA family of language models on WikiText, C4, and MMLU.

Yi-Hua Shao, Yang-Yang Gu, Min-Xi Yan et al. · 1 citation

Cross Domain Test Time Scaling: Scale Knowledge and Reasoning on Cross Domains

Cross-Domain TTS is proposed, a novel framework that enables task-tailored scaling in broader domains and achieves an improvement of up to 17% in pass@1 accuracy while reducing inference latency and saving up to 30% in token consumption.

Minxi Yan, Yihua Shao, Yanling Pan et al. · 0 citations

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