BEACON: Behavior and Appearance Control for Subject-Specific Video Generation

• URL: http://arxiv.org/abs/2609.13264v1
• Date: Mon, 07 Sep 2026 09:09:12 GMT
• Title: BEACON: Behavior and Appearance Control for Subject-Specific Video Generation
• Abstract summary: We introduce BEACON, a lightweight framework for person-specific video generation.<n>It produces more expressive videos by disentangling visual identity from expressive behavior.<n>By conditioning on these complementary signals, BEACON generates videos that better preserve both the subject's appearance and characteristic facial dynamics.
• Score: 20.360530698353916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating human-centric videos that preserve both visual identity and person-specific expressive behavior remains a fundamental challenge. In addition to reproducing appearance, a model must replicate the facial behaviors that characterize how a subject expresses emotion over time. However, most state-of-the-art methods condition generation on a single reference image, which contains no information about these temporal dynamics. As a result, they tend to preserve the subject's visual identity but often produce expressions with limited variation and weak subject specificity. To mitigate this issue, we introduce BEACON, a lightweight framework for person-specific video generation that produces more expressive videos by disentangling visual identity from expressive behavior. BEACON conditions generation on two complementary signals: a reference image encoding the identity and a reference video capturing subject-specific facial dynamics. By conditioning on these complementary signals, BEACON generates videos that better preserve both the subject's appearance and characteristic facial dynamics, while also supporting identity-expression transfer. Our experiments on the MEAD and RAVDESS datasets show that by fine-tuning on approximately 2,000 pairs and updating about 1% of the pretrained Wan video diffusion model, BEACON improves facial expressivity over state-of-the-art video generation methods while maintaining competitive identity preservation.

Related papers

• ID-V2V: Identity-Preserving Video Restylization [16.492389645021028]
  We formalize the challenge of identity-preserving video restylization, which propagates scene, lighting, and style changes specified by an edited video.<n>A key obstacle is the absence of paired training data, as identity-preserving restylized video pairs are rare in real-world settings.<n>We introduce ID-V2V, a video-to-video generative framework integrating complementary control signals.
  arXiv  Detail & Related papers  (2026-07-24T18:12:58Z)
• Slot-ID: Identity-Preserving Video Generation from Reference Videos via Slot-Based Temporal Identity Encoding [30.62635724018122]
  We present an identity-conditioned variant of a diffusion-transformer video generator.<n>A short clip reveals subject-specific patterns, e.g., how smiles form, across poses and lighting.<n>The approach consistently improves identity retention under large pose changes and expressive facial behavior.
  arXiv  Detail & Related papers  (2026-01-04T03:41:55Z)
• From Large Angles to Consistent Faces: Identity-Preserving Video Generation via Mixture of Facial Experts [69.44297222099175]
  We introduce a Mixture of Facial Experts (MoFE) that captures distinct but mutually reinforcing aspects of facial attributes.<n>To mitigate dataset limitations, we have tailored a data processing pipeline centered on two key aspects: Face Constraints and Identity Consistency.<n>We have curated and refined a Large Face Angles (LFA) dataset from existing open-source human video datasets.
  arXiv  Detail & Related papers  (2025-08-13T04:10:16Z)
• Mask-Free Audio-driven Talking Face Generation for Enhanced Visual Quality and Identity Preservation [54.52905471078152]
  We propose a mask-free talking face generation approach while maintaining the 2D-based face editing task.<n>We transform the input images to have closed mouths, using a two-step landmark-based approach trained in an unpaired manner.
  arXiv  Detail & Related papers  (2025-07-28T16:03:36Z)
• Removing Averaging: Personalized Lip-Sync Driven Characters Based on Identity Adapter [13.630889787934722]
  "lip averaging" phenomenon occurs when a model fails to preserve subtle facial details when dubbing unseen in-the-wild videos.<n>We propose UnAvgLip, which extracts identity embeddings from reference videos to generate highly faithful facial sequences.
  arXiv  Detail & Related papers  (2025-03-09T02:36:31Z)
• EchoVideo: Identity-Preserving Human Video Generation by Multimodal Feature Fusion [3.592206475366951]
  Existing methods struggle with "copy-paste" artifacts and low similarity issues.<n>We propose EchoVideo, which integrates high-level semantic features from text to capture clean facial identity representations.<n>It achieves excellent results in generating high-quality, controllability and fidelity videos.
  arXiv  Detail & Related papers  (2025-01-23T08:06:11Z)
• MEMO: Memory-Guided Diffusion for Expressive Talking Video Generation [55.95148886437854]
  Memory-guided EMOtion-aware diffusion (MEMO) is an end-to-end audio-driven portrait animation approach to generate talking videos.<n>MEMO generates more realistic talking videos across diverse image and audio types, outperforming state-of-the-art methods in overall quality, audio-lip synchronization, identity consistency, and expression-emotion alignment.
  arXiv  Detail & Related papers  (2024-12-05T18:57:26Z)
• VividPose: Advancing Stable Video Diffusion for Realistic Human Image Animation [79.99551055245071]
  We propose VividPose, an end-to-end pipeline that ensures superior temporal stability. An identity-aware appearance controller integrates additional facial information without compromising other appearance details. A geometry-aware pose controller utilizes both dense rendering maps from SMPL-X and sparse skeleton maps. VividPose exhibits superior generalization capabilities on our proposed in-the-wild dataset.
  arXiv  Detail & Related papers  (2024-05-28T13:18:32Z)
• Identity-Preserving Talking Face Generation with Landmark and Appearance Priors [106.79923577700345]
  Existing person-generic methods have difficulty in generating realistic and lip-synced videos. We propose a two-stage framework consisting of audio-to-landmark generation and landmark-to-video rendering procedures. Our method can produce more realistic, lip-synced, and identity-preserving videos than existing person-generic talking face generation methods.
  arXiv  Detail & Related papers  (2023-05-15T01:31:32Z)
• Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation [54.68893964373141]
  Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments in diffusion-based generative models allow for more realistic and stable data synthesis. We present an autoregressive diffusion model that requires only one identity image and audio sequence to generate a video of a realistic talking human head.
  arXiv  Detail & Related papers  (2023-01-06T14:16:54Z)
• Facial Expression Video Generation Based-On Spatio-temporal Convolutional GAN: FEV-GAN [1.279257604152629]
  We present a novel approach for generating videos of the six basic facial expressions. Our approach is based on Spatio-temporal Conal GANs, that are known to model both content and motion in the same network. The code and the pre-trained model will soon be made publicly available.
  arXiv  Detail & Related papers  (2022-10-20T11:54:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.