Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human Animation

• URL: http://arxiv.org/abs/2510.23581v1
• Date: Mon, 27 Oct 2025 17:50:19 GMT
• Title: Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human Animation
• Authors: Junyoung Seo, Rodrigo Mira, Alexandros Haliassos, Stella Bounareli, Honglie Chen, Linh Tran, Seungryong Kim, Zoe Landgraf, Jie Shen,
• Abstract summary: Lookahead Anchoring prevents identity drift during temporal autoregressive generation.<n>It transforms from fixed boundaries into directional beacons.<n>It also enables self-key-framing, where the reference image serves as the lookahead target.
• Score: 75.71558917038838
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Audio-driven human animation models often suffer from identity drift during temporal autoregressive generation, where characters gradually lose their identity over time. One solution is to generate keyframes as intermediate temporal anchors that prevent degradation, but this requires an additional keyframe generation stage and can restrict natural motion dynamics. To address this, we propose Lookahead Anchoring, which leverages keyframes from future timesteps ahead of the current generation window, rather than within it. This transforms keyframes from fixed boundaries into directional beacons: the model continuously pursues these future anchors while responding to immediate audio cues, maintaining consistent identity through persistent guidance. This also enables self-keyframing, where the reference image serves as the lookahead target, eliminating the need for keyframe generation entirely. We find that the temporal lookahead distance naturally controls the balance between expressivity and consistency: larger distances allow for greater motion freedom, while smaller ones strengthen identity adherence. When applied to three recent human animation models, Lookahead Anchoring achieves superior lip synchronization, identity preservation, and visual quality, demonstrating improved temporal conditioning across several different architectures. Video results are available at the following link: https://lookahead-anchoring.github.io.

Related papers

• MIBURI: Towards Expressive Interactive Gesture Synthesis [62.45332399212876]
  Embodied Conversational Agents (ECAs) aim to emulate human face-to-face interaction through speech, gestures, and facial expressions.<n>Existing solutions for ECAs produce rigid, low-diversity motions that are unsuitable for human-like interaction.<n>We present MIBURI, the first online, causal framework for generating expressive full-body gestures and facial expressions synchronized with real-time spoken dialogue.
  arXiv  Detail & Related papers  (2026-03-03T18:59:51Z)
• Knot Forcing: Taming Autoregressive Video Diffusion Models for Real-time Infinite Interactive Portrait Animation [16.692450893925148]
  We present a novel streaming framework named Knot Forcing for real-time portrait animation.<n>K Knot Forcing enables high-fidelity, temporally consistent, and interactive portrait animation over infinite sequences.
  arXiv  Detail & Related papers  (2025-12-25T16:34:56Z)
• STARCaster: Spatio-Temporal AutoRegressive Video Diffusion for Identity- and View-Aware Talking Portraits [44.82339975771063]
  STARCaster is an identity-aware video diffusion model that addresses both speech-driven portrait animation and free-point-view talking portrait.<n>The model learns from longer temporal contexts than those generated at inference, mitigating the overly static animations common in existing autoregressive approaches.
  arXiv  Detail & Related papers  (2025-12-15T11:59:01Z)
• AlcheMinT: Fine-grained Temporal Control for Multi-Reference Consistent Video Generation [58.844504598618094]
  We propose AlcheMinT, a unified framework that introduces explicit timestamps conditioning for subject-driven video generation.<n>Our approach introduces a novel positional encoding mechanism that unlocks the encoding of temporal intervals, associated in our case with subject identities.<n>We incorporate subject-descriptive text tokens to strengthen binding between visual identity and video captions, mitigating ambiguity during generation.
  arXiv  Detail & Related papers  (2025-12-11T18:59:34Z)
• Audio Driven Real-Time Facial Animation for Social Telepresence [65.66220599734338]
  We present an audio-driven real-time system for animating photorealistic 3D facial avatars with minimal latency.<n>Central to our approach is an encoder model that transforms audio signals into latent facial expression sequences in real time.<n>We capture the rich spectrum of facial expressions necessary for natural communication while achieving real-time performance.
  arXiv  Detail & Related papers  (2025-10-01T17:57:05Z)
• Stable Video-Driven Portraits [52.008400639227034]
  Animation aims to generate photo-realistic videos from a single source image by reenacting the expression and pose from a driving video.<n>Recent advances using diffusion models have demonstrated improved quality but remain constrained by weak control signals and architectural limitations.<n>We propose a novel diffusion based framework that leverages masked facial regions specifically the eyes, nose, and mouth from the driving video as strong motion control cues.
  arXiv  Detail & Related papers  (2025-09-22T08:11:08Z)
• AvatarSync: Rethinking Talking-Head Animation through Phoneme-Guided Autoregressive Perspective [15.69417162113696]
  AvatarSync is an autoregressive framework on phoneme representations that generates realistic talking-head animations from a single reference image.<n>We show that AvatarSync outperforms existing talking-head animation methods in visual fidelity, temporal consistency, and computational efficiency.
  arXiv  Detail & Related papers  (2025-09-15T15:34:02Z)
• InfiniteTalk: Audio-driven Video Generation for Sparse-Frame Video Dubbing [66.48064661467781]
  We introduce sparse-frame video dubbing, a novel paradigm that strategically preserves references to maintain identity, iconic gestures, and camera trajectories.<n>We propose InfiniteTalk, a streaming audio-driven generator designed for infinite-length long sequence dubbing.<n> Comprehensive evaluations on HDTF, CelebV-HQ, and EMTD datasets demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-08-19T17:55:23Z)
• KeyFace: Expressive Audio-Driven Facial Animation for Long Sequences via KeyFrame Interpolation [37.27908280809964]
  KeyFace is a novel two-stage diffusion-based framework for facial animation.<n>In the first stage, a model fills in the gaps between transitions, ensuring smooth and temporal coherence.<n>To further enhance realism, we incorporate continuous emotion representations and handle a wide range of non-speech vocalizations (NSVs)<n> Experimental results show that KeyFace outperforms state-of-the-art methods in generating natural, coherent facial animations over extended durations.
  arXiv  Detail & Related papers  (2025-03-03T16:31:55Z)
• UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animation [53.16986875759286]
  We present a UniAnimate framework to enable efficient and long-term human video generation. We map the reference image along with the posture guidance and noise video into a common feature space. We also propose a unified noise input that supports random noised input as well as first frame conditioned input.
  arXiv  Detail & Related papers  (2024-06-03T10:51:10Z)
• Towards Smooth Video Composition [59.134911550142455]
  Video generation requires consistent and persistent frames with dynamic content over time. This work investigates modeling the temporal relations for composing video with arbitrary length, from a few frames to even infinite, using generative adversarial networks (GANs) We show that the alias-free operation for single image generation, together with adequately pre-learned knowledge, brings a smooth frame transition without compromising the per-frame quality.
  arXiv  Detail & Related papers  (2022-12-14T18:54:13Z)

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.