Xiaodong Cun

Yiguo Jiang, Xiaodong Cun, Yong Zhang et al.

Emotional talking head synthesis aims to generate talking portrait videos with vivid expressions. Existing methods still exhibit limitations in control flexibility, motion naturalness, and expression quality. Moreover, currently available datasets are primarily collected in lab settings, further exacerbating these shortcomings. Consequently, these limitations substantially hinder practical applications in real-world scenarios. To address these challenges, we propose EmoCAST, a diffusion-based framework with two key modules for precise text-driven emotional synthesis. In appearance modeling, emotional prompts are integrated through a text-guided decoupled emotive module, enhancing the spatial knowledge to improve emotion comprehension. To improve the relationship between audio and emotion, we introduce an emotive audio attention module to capture the interplay between controlled emotion and driving audio, generating emotion-aware features to guide more precise facial motion synthesis. Additionally, we construct an emotional talking head dataset with comprehensive emotive text descriptions to optimize the framework's performance. Based on the proposed dataset, we propose an emotion-aware sampling training strategy and a progressive functional training strategy that further improve the model's ability to capture nuanced expressive features and achieve accurate lip-synchronization. Overall, EmoCAST achieves state-of-the-art performance in generating realistic, emotionally expressive, and audio-synchronized talking-head videos. Project Page: https://github.com/GVCLab/EmoCAST

Yihao Zhi, Chenghong Li, Hongjie Liao et al.

Recent breakthroughs in video generation, powered by large-scale datasets and diffusion techniques, have shown that video diffusion models can function as implicit 4D novel view synthesizers. Nevertheless, current methods primarily concentrate on redirecting camera trajectory within the front view while struggling to generate 360-degree viewpoint changes. In this paper, we focus on human-centric subdomain and present MV-Performer, an innovative framework for creating synchronized novel view videos from monocular full-body captures. To achieve a 360-degree synthesis, we extensively leverage the MVHumanNet dataset and incorporate an informative condition signal. Specifically, we use the camera-dependent normal maps rendered from oriented partial point clouds, which effectively alleviate the ambiguity between seen and unseen observations. To maintain synchronization in the generated videos, we propose a multi-view human-centric video diffusion model that fuses information from the reference video, partial rendering, and different viewpoints. Additionally, we provide a robust inference procedure for in-the-wild video cases, which greatly mitigates the artifacts induced by imperfect monocular depth estimation. Extensive experiments on three datasets demonstrate our MV-Performer's state-of-the-art effectiveness and robustness, setting a strong model for human-centric 4D novel view synthesis.