The paper introduces AVAPrintDB, a new public multi‑generator talking‑head avatar database designed for avatar fingerprinting, comprising data from two audiovisual corpora and three state‑of‑the‑art generators (GAGAvatar, LivePortrait, HunyuanPortrait). It also defines a standardized benchmark that evaluates existing avatar fingerprinting systems and explores new methods based on Foundation Models such as DINOv2 and CLIP, while analyzing performance under generator and dataset shift. The authors find that identity‑related motion cues persist across synthetic avatars, yet current fingerprinting systems are highly sensitive to changes in synthesis pipelines and source domains.
arXiv:2609.39273v1 Announce Type: new Abstract: This report presents \textbf{MegaAvatar}, a controllable talking avatar generation framework built on top of the Wan2.2-TI2V-5B model. Compared with pr...
arXiv:2511.17747v2 Announce Type: replace-cross Abstract: The growing adoption of photorealistic 3D facial avatars, particularly those utilizing efficient 3D Gaussian Splatting representations, intro...
arXiv:2610.08417v1 Announce Type: new Abstract: Recent advances in LipSync generation technology have led to the creation of highly realistic videos, posing severe societal risks. However, existing d...
Vorch-Human is a unified framework for human‑centric audio‑visual generation that handles multiple tasks—animating a person from speech, jointly generating speech and video from a voice reference, and synthesizing a scene from paired appearance and voice references—using a single dual‑stream audio‑video diffusion transformer. The model incorporates clean condition‑audio and condition‑video tokens, per‑token task embeddings, temporal position types, condition masks, and a shared multimodal prompt encoder to express diverse inputs such as driving speech, timbre examples, first frames, and subject images. A two‑level data pipeline supplies the necessary supervision by extracting speech, appearance, and timbre annotations from clips and linking consistent identity and outfit references across videos, while a frozen‑prefix recurrence enables long‑form audio‑driven generation with reduced boundary discontinuity and identity drift.
arXiv:2608.30951v1 Announce Type: new Abstract: The rapid development of generative portrait models has raised growing concerns about privacy leakage and identity misuse. In particular, audio-driven...
Recent advances in LipSync generation technology have led to the creation of highly realistic videos, posing severe societal risks. However, existing defense strategies struggle against LipSync forger...
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...
The article critiques the notion that a person's voice is a stable, unique biometric trace—termed a voiceprint—by reviewing historical, forensic, and technological evidence. It argues that voices are highly dynamic and context-dependent, and that the voiceprint metaphor misrepresents probabilistic speaker information as a fixed identity marker. The authors emphasize that speaker recognition should account for within-speaker variability, domain mismatch, and synthetic manipulation rather than rely on an assumed stable voiceprint.
arXiv:2610.35414v1 Announce Type: new Abstract: Photorealistic avatar videos decouple the appearance of a rendered face from the person who operates it, which makes conventional face recognition unsu...
arXiv:2610.19717v1 Announce Type: new Abstract: Recent advances in diffusion models have enabled high-fidelity, identity-preserving image generation for personalized applications such as digital avat...
The paper introduces Anti-Persona, an image-level defense that prevents unauthorized identity binding and recognition in personalized vision–language models. By aggregating visual features from multiple reference images into an identity prototype and applying subtle perturbations, the method disrupts prototype alignment in the vision‑encoder space while maintaining visual fidelity. Experiments on two personalized LVLMs show protection rates up to 95.0% and robustness across prompt variations and encoder mismatches.
arXiv:2601. 00664v2 Announce Type: replace-cross Abstract: Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation.