Identity-preserving video generation (IPVG) aims to synthesize high-fidelity videos that follow text prompts while faithfully preserving a reference identity. Despite recent progress, existing IPVG methods still struggle to balance high-level semantic control and low-level identity fidelity. To bridge this gap, we propose ST-DRC, an effective Spatial-Temporal Decoupled Reference Conditioning framework for identity-preserving text-to-video generation. At the framework level, ST-DRC performs latent in-context feature injection by encoding the reference image with the video VAE and concatenating it with noisy video latents, enabling rich low-level identity details to be accessed without additional adapters. To separate identity-aware reference retrieval from appearance copying, we introduce TASS-RoPE, a Temporal-Adjacent Spatial-Shifted RoPE scheme that places reference tokens near the video sequence in time but shifts them in space, allowing reference information to flow through spatio-temporal attention while suppressing pixel-level copy-paste shortcuts. To further prevent shortcut learning and strengthen the otherwise diluted identity supervision in the diffusion objective, we combine appearance-invariant reference augmentation with face-guided identity objectives, encouraging the model to preserve identity under variations in color, pose, and layout. At inference time, we introduce a three-stream reference classifier-free guidance strategy that independently controls text adherence and reference fidelity. Experiments demonstrate that ST-DRC achieves strong identity preservation, prompt alignment, temporal consistency, and video quality with a lightweight design built on LTX-2.3. Our method ranks among the top submissions in the facial identity-preserving video generation track, validating the effectiveness of spatial-temporal decoupled reference conditioning.
GeoBridge: Decoupled Semantic Conditioning for Generative Image Geolocalization
University of Chinese Academy of Sciences（中国科学院大学）；School of Advanced Interdisciplinary Sciences（先进交叉科学学院）；School of Electronic, Electrical and Communication Engineering（电子电气与通信工程学院）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究院）
Verily Life Sciences（Verily生命科学）；Technion（以色列理工学院）；MIT（麻省理工学院）
Training a generative model on a single image has drawn significant attention in recent years. Single image generative methods are designed to learn the internal patch distribution of a single natural image at multiple scales. These models can be used for drawing diverse samples that semantically resemble the training image, as well as for solving many image editing and restoration tasks that involve that particular image. Here, we introduce an extended framework, which allows to simultaneously learn the internal distributions of several images, by using a single model with spatially varying i…
Beyond Inference Intervention: Identity-Decoupled Diffusion for Face Anonymization
School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；School of Future Technology, South China University of Technology（华南理工大学未来技术学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理学院计算机与信息学院）
IM-Animation: An Implicit Motion Representation for Identity-decoupled Character Animation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Kling Team, Kuaishou Technology（快手技术 Kling 团队）；Cardiff University（卡迪夫大学）
LILAC: Layer-Wise Independent LoRAs and Cascaded Conditioning for Multi-Concept Customization of Diffusion Models
Adobe Research, Romania（罗马尼亚Adobe研究院）；Department of Computer Science, University of Bucharest, Romania（罗马尼亚布加勒斯特大学计算机科学系）；International Computer High School of Bucharest, Romania（罗马尼亚布加勒斯特国际计算机高中）
Inria（法国国家信息与自动化研究所）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；University of Oxford（牛津大学）
Data-driven generative 3D face models are used to compactly encode facial shape data into meaningful parametric representations. A desirable property of these models is their ability to effectively decouple natural sources of variation, in particular identity and expression. While factorized representations have been proposed for that purpose, they are still limited in the variability they can capture and may present modeling artifacts when applied to tasks such as expression transfer. In this work, we explore a new direction with Generative Adversarial Networks and show that they contribute t…
RGFVR: Reference-Guided Face Video Restoration with Flow Matching
Chair of Media Technology, Munich Institute of Robotics and Machine Intelligence, School of Computation, Information, and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院慕尼黑机器人与机器智能研究所媒体技术教席）
AnyCrowd: Instance-Isolated Identity-Pose Binding for Arbitrary Multi-Character Animation
Mohamed bin Zayed University of Artificial Intelligence, UAE（穆罕默德·本·扎耶德人工智能大学，阿联酋）；University of Tromsø (UiT) – The Arctic University of Norway, Norway（特罗姆瑟大学（UiT）——挪威北极大学，挪威）；Shenzhen campus of Sun Yet-sen University, China（孙中山大学深圳校区，中国）
Frequency Routing and Identity Conditioning for Thermal-to-Visible Face Translation
Monash University, Monash Suzhou Research Institute（莫纳什大学、莫纳什大学苏州研究院）；Southeast University, School of Computer Science and Engineering（东南大学计算机科学与工程学院）；Monash University, Faculty of Information Technology（莫纳什大学信息技术学院）
