arXiv AI
5d ago

SOMA: A Benchmark for Fine-Grained Egocentric Behavior and Body-Motion Understanding

SOMA is a new benchmark that tests fine‑grained egocentric body‑motion understanding, featuring 1,632 human‑verified questions across body configuration, motion dynamics, and temporal reasoning. The study evaluates 37 multimodal large language models (MLLMs) and finds that humans achieve 68.5% accuracy while the best MLLM (Qwen3.6‑27B) reaches only 50.0% zero‑shot accuracy. The authors also provide a training dataset and a multimodal baseline to help close the performance gap and highlight common failure modes such as misreading body parts and temporal errors.

By Matea Tashkovska, Na\"el Ouerghemi, Haozhe Qi, Alexander Mathis
arXiv Computer Vision
Aug 28

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

HUG‑VIS is a unified multimodal benchmark for human‑centered visual intelligence, comprising 8,400 half‑body videos of 30 professional actors performing 280 emotion‑action prompts in Mandarin. The dataset provides synchronized video, audio, text, and alpha mattes for four tasks—human emotion recognition, video generation, voice cloning, and video matting—allowing evaluation of both open‑ and closed‑source models under a zero‑shot protocol. Results reveal that linguistic cues dominate emotion recognition, visual affect is weakest, and that automatic metrics and human judgments diverge in generation and cloning tasks, while motion‑related boundary fidelity remains a key challenge for matting.

By Fei Ma, Zebang Cheng, Minghui Li, Hongbo Xu, Yuyong Tan, Yihua Shao, Hanling Wang, Zhou Liu, Yuqing Gao, Dong Wang, Long Ma, Laizhong Cui, Nicu Sebe, Qi Tian
arXiv AI
5d ago

CLEAR: Coupling Local Evidence with Affective Representations for Multimodal Emotion Recognition in Conversation

The paper introduces CLEAR, a framework for Multimodal Emotion Recognition in Conversation that couples contextual representations with local discriminative cues. It replaces emotion label generation with continuous internal representations from a vision‑language model and trains a classifier on these representations. Sample‑adaptive residual coupling is used to integrate textual and acoustic evidence from the target utterance into the contextual representation, improving emotion recognition performance on IEMOCAP and MELD datasets.

By Linan Zhu, Zihao Zhai, Mengfei Xu, Xiao Han, Yuqian Fu, Xiangfan Chen, Zihang Zhang