This paper introduces a Conditional Variational Autoencoder (CVAEs) approach that generates realistic, controllable emotional facial expressions for virtual humans. Trained on a small dataset of 7,680 samples covering six basic emotions at low and high intensity, the model learns latent representations that preserve key expressive characteristics across intensity levels. The method enables animators to produce emotionally expressive virtual characters without actor performances or manual artistic effort.
By Vitor Miguel Xavier Peres, Lara Volpato, Gabriel Ferri Scnheider, Soraia Raupp Musse
Video2Reaction is a multimodal dataset that links short movie segments to the emotional reactions of viewers, gathered from social media comments. The dataset models reactions as distributions over categorical emotions, capturing the subjective and ambiguous nature of emotional perception. Experiments show that vision‑language models fine‑tuned with LoRA learn effectively from Video2Reaction and outperform specialized baselines, and that models pre‑fine‑tuned on this dataset transfer well to other emotion prediction tasks.
By Sidong Zhang, Trang Nguyen, Shiv Shankar, Gauri Jagatap, Deepak Chandran, Andrea Fanelli, Madalina Fiterau
arXiv:2609.13264v1 Announce Type: cross
Abstract: Generating human-centric videos that preserve both visual identity and person-specific expressive behavior remains a fundamental challenge. In additi...
By Pokrzywa Baptiste, Nabyl Quignon, Yara Bahram, Muhammad Osama Zeeshan, Antitza Dantcheva, Eric Granger
The paper presents Test-Time Adaptation via Cache Personalization (TTA‑CaP), a gradient‑free, cache‑based method that personalizes vision‑language models for facial expression recognition in videos. TTA‑CaP uses three complementary caches—a personalized static cache, a positive target cache, and a negative target cache—controlled by a tri‑gate mechanism to prevent corruption and provide robust subject‑matched evidence. Experiments on BioVid, StressID, and BAH datasets show that TTA‑CaP outperforms state‑of‑the‑art test‑time adaptation methods while keeping computational and memory overhead low.
By Masoumeh Sharafi, Muhammad Osama Zeeshan, Soufiane Belharbi, Alessandro Lameiras Koerich, Marco Pedersoli, Eric Granger
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:2606. 28769v1 Announce Type: new Abstract: Emotional body motion expressions are an essential element of non-verbal communication.
By Huakun Liu, Miao Cheng, Xin Wei, Felix Dollack, Victor Schneider, Hideaki Uchiyama, Chia-huei Tseng, Yoshifumi Kitamura, Monica Perusquia-Hernandez