Vera: Identity-Faithful Human Subject-to-Video Generation

• URL: http://arxiv.org/abs/2607.20247v1
• Date: Wed, 22 Jul 2026 15:08:30 GMT
• Title: Vera: Identity-Faithful Human Subject-to-Video Generation
• Abstract summary: We propose Vera, a unified human-centric S2V framework for single- and multi-person generation.<n>We first construct a million-pair identity-aligned human image-video dataset through person-level cross-clip retrieval.<n>We show that Vera improves human identity consistency, multi-person subject binding, and motion naturalness, while reducing identity confusion and excessive reference-image copying.
• Score: 66.45205582225942
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Subject-to-video (S2V) generation has made substantial progress in preserving reference subjects across diverse categories, yet generic subject consistency remains insufficient for human-centric generation. A video may appear globally consistent while identity-critical human details still drift across frames, poses, and interactions. This issue becomes more severe in multi-person scenarios, where incorrect identity-role binding leads to subject confusion, attribute swapping, and excessive copying of reference-specific appearance cues. We propose Vera, a unified human-centric S2V framework for single- and multi-person generation. We first construct a million-pair identity-aligned human image-video dataset through person-level cross-clip retrieval, providing explicit identity correspondence and diverse references. Built on this dataset, Vera introduces two complementary designs. Identity-Focal Masked Supervision (IFMS) strengthens identity-aware learning with spatially focused supervision while reducing interference from irrelevant artifacts. Reference-Aware Layer-wise Attention (RALA) regulates how video tokens interact with reference identity cues in the DiT backbone, preserving stable identity anchors and enhancing layer-aware identity readout. Extensive experiments demonstrate that Vera improves human identity consistency, multi-person subject binding, and motion naturalness, while reducing identity confusion and excessive reference-image copying.

Related papers

• GroupVideo: Multi-Identity Customized Text-to-Video Generation [53.89036635198158]
  GroupVideo is a novel framework that leverages multiple individual photographs to generate identitycustomized video.<n>GroupVideo incorporates multimodal identity alignment, while semantic alignment introduces a semantic perceiver to enhance the naturalness of motions.<n>In response to the shortage of multi-ID video datasets, we have curated a comprehensive high-quality dataset of 20,000 videos.
  arXiv  Detail & Related papers  (2026-07-23T08:09:04Z)
• Customizing Video Portraits via Identity-ActionDecoupling [32.57069491938664]
  Identity-Preserving Text-to-Video Generation (IPT2V) seeks to synthesize a temporally coherent video from a reference image and a textual description.<n>We introduce Identity-Action Decoupling framework as well as two loss function Identity Decoupling Loss and Text Alignment Loss to solve this problem.
  arXiv  Detail & Related papers  (2026-06-21T05:56:28Z)
• HarmoView: Harmonizing Multi-View Constraints for Identity-Consistent Video Generation [21.0568663910476]
  HarmoView is a robust framework for identity-consistent video generation.<n>It integrates multi-view cues through three architectural refinements and a staged training curriculum.<n>HarmoView significantly outperforms open-source baselines and matches leading closed-source engines.
  arXiv  Detail & Related papers  (2026-06-09T13:26:39Z)
• FaithfulFaces: Pose-Faithful Facial Identity Preservation for Text-to-Video Generation [28.523984146967805]
  Identity-preserving text-to-video generation (IPT2V) empowers users to produce diverse and imaginative videos with consistent human facial identity.<n>We propose textitFaithfulFaces, a pose-faithful facial identity preservation learning framework to improve IPT2V in complex dynamic scenes.
  arXiv  Detail & Related papers  (2026-05-06T09:54:09Z)
• AnyCrowd: Instance-Isolated Identity-Pose Binding for Arbitrary Multi-Character Animation [55.94507360511886]
  We propose AnyCrowd, a Transformer-based video generation framework capable of scaling to an arbitrary number of characters.<n>Specifically, we first introduce an Instance-Isolated Latent Representation (IILR), which encodes character instances independently prior to DiT processing to prevent latent identity entanglement.<n>Building on this disentangled representation, we further propose Tri-Stage Decoupled Attention (TSDA) to bind identities to driving poses by decomposing self-attention into: (i) instance-aware foreground attention, (ii) background-centric interaction, and (iii) global-background coordination.
  arXiv  Detail & Related papers  (2026-03-16T15:25:04Z)
• IdentityStory: Taming Your Identity-Preserving Generator for Human-Centric Story Generation [75.09818147405898]
  IdentityStory is a framework for human-centric story generation that ensures consistent character identity across sequential images.<n>By taming identity-preserving generators, the framework features two key components: Iterative Identity Discovery and Re-denoising Identity Injection.
  arXiv  Detail & Related papers  (2025-12-29T14:54:44Z)
• WithAnyone: Towards Controllable and ID Consistent Image Generation [83.55786496542062]
  Identity-consistent generation has become an important focus in text-to-image research.<n>We develop a large-scale paired dataset tailored for multi-person scenarios.<n>We propose a novel training paradigm with a contrastive identity loss that leverages paired data to balance fidelity with diversity.
  arXiv  Detail & Related papers  (2025-10-16T17:59:54Z)
• StableIdentity: Inserting Anybody into Anywhere at First Sight [57.99693188913382]
  We propose StableIdentity, which allows identity-consistent recontextualization with just one face image. We are the first to directly inject the identity learned from a single image into video/3D generation without finetuning.
  arXiv  Detail & Related papers  (2024-01-29T09:06:15Z)
• T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up [16.165889084870116]
  We present an end-to-end approach to generate high-resolution person images conditioned on texts only. We develop an effective generative model to produce person images with two novel mechanisms.
  arXiv  Detail & Related papers  (2022-08-18T07:41:02Z)

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.