Jiahe Zhang

Youliang Zhang, Zhaoyang Li, Duomin Wang et al.

The rapid development of large-scale models has catalyzed significant breakthroughs in the digital human domain. These advanced methodologies offer high-fidelity solutions for avatar driving and rendering, leading academia to focus on the next major challenge: audio-visual dyadic interactive virtual human. To facilitate research in this emerging area, we present SpeakerVid-5M dataset, the first large-scale, high-quality dataset designed for audio-visual dyadic interactive virtual human generation. Totaling over 8,743 hours, SpeakerVid-5M contains more than 5.2 million video clips of human portraits. It covers diverse scales and interaction types, including monadic talking, listening, and dyadic conversations. Crucially, the dataset is structured along two key dimensions: interaction type and data quality. First, it is categorized into four types (dialogue branch, single branch, listening branch and multi-turn branch) based on the interaction scenario. Second, it is stratified into a large-scale pre-training subset and a curated, high-quality subset for Supervised Fine-Tuning (SFT). This dual structure accommodates a wide array of 2D virtual human tasks. In addition, we provide an autoregressive (AR)-based video chat baseline trained on this data, accompanied by a dedicated set of metrics and test data to serve as a benchmark VidChatBench for future work. Both the dataset and the corresponding data processing code will be publicly released. Project page: https://dorniwang.github.io/SpeakerVid-5M/

Yaning Zhang, Jiahe Zhang, Chunjie Ma et al.

The challenge of tracing the source attribution of forged faces has gained significant attention due to the rapid advancement of generative models. However, existing deepfake attribution (DFA) works primarily focus on the interaction among various domains in vision modality, and other modalities such as texts and face parsing are not fully explored. Besides, they tend to fail to assess the generalization performance of deepfake attributors to unseen advanced generators like diffusion in a fine-grained manner. In this paper, we propose a novel parsing-aware vision language model with dynamic contrastive learning(PVLM) method for zero-shot deepfake attribution (ZS-DFA),which facilitates effective and fine-grained traceability to unseen advanced generators. Specifically, we conduct a novel and fine-grained ZS-DFA benchmark to evaluate the attribution performance of deepfake attributors to unseen advanced generators like diffusion. Besides, we propose an innovative parsing-guided vision language model with dynamic contrastive learning (PVLM) method to capture general and diverse attribution features. We are motivated by the observation that the preservation of source face attributes in facial images generated by GAN and diffusion models varies significantly. We employ the inherent face attributes preservation differences to capture face parsing-aware forgery representations. Therefore, we devise a novel parsing encoder to focus on global face attribute embeddings, enabling parsing-guided DFA representation learning via dynamic vision-parsing matching. Additionally, we present a novel deepfake attribution contrastive center loss to pull relevant generators closer and push irrelevant ones away, which can be introduced into DFA models to enhance traceability. Experimental results show that our model exceeds the state-of-the-art on the ZS-DFA benchmark via various protocol evaluations.