arXiv Computer Vision
Aug 21
ID-V2V: Identity-Preserving Video Restylization
arXiv:2607. 22830v2 Announce Type: replace Abstract: In visual storytelling, human performances are central to creative intent and narrative meaning.
BEACON: Behavior and Appearance Control for Subject-Specific Video Generation
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Hugging Face Trending Papers
Jul 23
GroupVideo: Multi-Identity Customized Text-to-Video Generation
Current identity customized video generation methodologies are predominantly limited to single-identity scenarios, as the lack of explicit identity separation mechanisms often leads to identity confusion in multi-identity settings. Existing multi-identity approaches, which directly extend single-identity frameworks by concatenating face images as input conditions, frequently result in unnatural facial expressions and motions, manifesting as the "copy-paste" phenomenon.
arXiv AI
Jun 11
ARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation
arXiv:2606. 11670v1 Announce Type: cross Abstract: Subject-preserving video generation is not solved by frontal-face similarity alone: a generated person must remain recognizable across motion, large viewpoint changes, expression shifts, occlusion, scale variation, and conflicts among text, first-frame, and identity references.
arXiv AI
Jul 21
Identity-Consistent Expression Fields: A Disentangled Neural Radiance Field Framework for Few-Shot Facial Expression Synthesis
arXiv:2607. 16287v1 Announce Type: cross Abstract: Neural Radiance Fields (NeRF) have enabled photorealistic novel-view synthesis of 3D scenes and, in the facial domain, have been extended to reconstruct and animate 3D face models from a small number of images.
arXiv AI
Jun 2
Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation
arXiv:2601. 00664v2 Announce Type: replace-cross Abstract: Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation.
arXiv Computation and Language
Sep 4
Chehre: An Emoji-Prompted Dataset to Explore Perceptual Flexibility in Video Language Models
Chehre is an emoji‑prompted video dataset designed to study perceptual flexibility in video language models. It contains 2,111 videos of 203 participants expressing 40 facial emojis, with each video annotated by about 30 perceivers, yielding 1,242 annotators in total. The dataset introduces a new task—distributional expression recognition—that evaluates a model’s ability to reproduce the variation seen in human annotations, and shows that persona prompting can shift model perception to better match human variability.
arXiv Computer Vision
Aug 25
DiffSwap++: 3D Latent-Controlled Diffusion for Identity-Preserving Face Swapping
arXiv:2511.05575v2 Announce Type: replace Abstract: Diffusion-based approaches have recently achieved strong results in face swapping, offering improved visual quality over traditional GAN-based meth...
arXiv Computer Vision
Aug 25
Emotion Intensity Matters: Generating Realistic Expressions in Virtual Humans with CVAEs
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.
arXiv AI
Jul 7
Motion Attribution for Video Generation
arXiv:2601. 08828v2 Announce Type: replace-cross Abstract: Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood.
Hugging Face Trending Papers
Aug 20
WithEveryone: Unified Planning and Identity Grounding for Group Image Generation
Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every reference to a distinct person and location, while training-time identity losses must establish correspondence among several noisy predicted faces.
arXiv Computer Vision
Sep 11
DirectSwap: Paired, Mask-Free Video Head Swapping with Full-Reference Evaluation
The paper introduces DirectSwap, a mask‑free video head‑swapping method that leverages a newly created cross‑identity paired dataset, HeadSwapBench. By synthesizing expression‑synchronized video pairs from real footage, the authors provide frame‑aligned ground truth for full‑reference evaluation of identity, expression, pose, reconstruction fidelity, and temporal stability. DirectSwap outperforms traditional same‑identity masked reconstruction, especially when head silhouettes change, and can restore non‑head content without external segmentation.
Hugging Face Trending Papers
Jul 13
Latent-Identity Tuning in Text-to-Image Personalization Models
Generating and editing a person's face demands high precision, as even minor modifications can significantly alter a subject's perceived identity. Current personalization and editing methods built on general-purpose text-to-image models, however, often lack the precision required for fine-grained facial edits.