Annotated Papers

Foundations: Image Generating Models

这一节只保留后文需要反复引用的生成模型、条件控制、RL post-training 和 reward modeling 基础。

Diffusion Models

  • Paper Title: Denoising Diffusion Probabilistic Models [1] TL;DR: 提出了 DDPM 的前向加噪和反向去噪训练范式，是后续 diffusion image generation/editing 的基本公式来源。
  • Paper Title: Denoising Diffusion Implicit Models [2] TL;DR: 用非马尔可夫/确定性采样路径加速 diffusion sampling，也为 DDIM inversion 和图像编辑中的内容保持提供基础工具。
  • Paper Title: High-Resolution Image Synthesis with Latent Diffusion Models [3] TL;DR: 将扩散过程移到 autoencoder latent space 中，大幅降低高分辨率图像生成和编辑的训练、采样成本。
  • Paper Title: Scalable Diffusion Models with Transformers [4] TL;DR: 用 transformer patch denoiser 替代 U-Net，说明 diffusion backbone 可以沿着 transformer scaling law 扩展。
  • Paper Title: Qwen-Image-2.0 Technical Report [5] TL;DR: 代表近期大规模 multimodal image generation/editing 系统，可作为前沿 diffusion/flow-style 图像基础模型的背景材料。

Flow Matching Models

  • Paper Title: Flow Matching for Generative Modeling [6] TL;DR: 将生成建模表述为学习从 base distribution 到 data distribution 的连续向量场，是 flow matching 的核心理论基础。
  • Paper Title: An Introduction to Flow Matching and Diffusion Models [7] TL;DR: 系统梳理 flow matching 与 diffusion 的联系，适合在综述中作为统一 notation 和直觉解释。

Special Note: DiffAE

  • Paper Title: Diffusion Autoencoders: Toward a Meaningful and Decodable Representation [8] TL;DR: 将图像分解为 semantic code 和 stochastic code，为身份保持、属性编辑和人脸 relighting 提供可控的 latent 表示。

Special Note: RL, Reward Modeling, and Post-Training

  • Paper Title: Flow-GRPO: Training Flow Matching Models via Online RL [9] TL;DR: 将 flow sampler 改写为可优化的随机策略，并用 group-relative reward 进行在线 RL post-training。
  • Paper Title: ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation [10] TL;DR: 学习人类偏好的 text-to-image reward model，是后续图像生成 RL 和自动评价的重要基础。
  • Paper Title: SpatialReward: Verifiable Spatial Reward Modeling for Fine-Grained Spatial Consistency in Text-to-Image Generation [11] TL;DR: 针对空间关系构造可验证 reward，可用于讨论细粒度 layout、位置和多主体一致性的奖励设计。
  • Paper Title: Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation [12] TL;DR: 关注 reward model 鲁棒性和 faithful editing，对后文 instruction-following editing 的 RL 训练有直接关联。
  • Paper Title: Leveraging Verifier-Based Reinforcement Learning in Image Editing [13] TL;DR: 将 verifier-based reward 引入图像编辑，说明 editing model 可以通过可检查目标进行强化学习优化。
  • Paper Title: EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling [14] TL;DR: 为 image editing 构造更高保真的 reward model，使在线 RL 能更稳定地优化编辑质量和指令一致性。
  • Paper Title: SFT, RL, and On-Policy Distillation Through a Distributional Lens [15] TL;DR: 从 distributional 角度解释 SFT、RL 和 on-policy distillation，可作为生成模型 post-training 的概念补充。

Topic 1: Group Portrait Editing

这一节可以围绕多主体身份保持、主体区分、插入/删除/操控、群体 harmonization 和 instruction following 展开。

Task-Specific Group Portrait Editing

  • Paper Title: GroupDiff: Diffusion-based Group Portrait Editing [16] TL;DR: 专门面向群体合照编辑，重点处理多人物编辑中的身份保持、局部修改和整体一致性问题。
  • Paper Title: Insert Anyone: High-fidelity Full-body Photo Insertion via Dual-branch Adapters [17] TL;DR: 针对全身人物插入任务设计双分支 adapter，同时考虑目标人物身份/外观保持和场景融合。
  • Paper Title: Ingredients: Blending Custom Photos with Video Diffusion Transformers [18] TL;DR: 使用 video diffusion transformer 融合自定义照片素材，可用于讨论多主体/多素材组合生成中的 temporal 或 structural consistency。
  • Paper Title: SmartPhotoCrafter: Unified Reasoning, Generation and Optimization for Automatic Photographic Image Editing [19] TL;DR: 将推理、生成和优化统一到自动摄影图像编辑流程，可作为 instruction-driven portrait/group editing 的系统级方法。

ID Consistency and Multi-ID Disentanglement

  • Paper Title: WithAnyone: Towards Controllable and ID Consistent Image Generation [20] TL;DR: 关注可控且身份一致的人像生成，可作为单人到多人 ID-preserving editing 的基础方法。
  • Paper Title: InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity [21] TL;DR: 面向身份保持的 photo recrafting，适合放在 ID consistency 和 high-fidelity personalization 讨论中。
  • Paper Title: MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware Alignment and Disentanglement [22] TL;DR: 针对多主体个性化生成提出 correspondence-aware alignment 和 disentanglement，核心问题是避免不同人物身份混淆。
  • Paper Title: ID-Patch: Robust ID Association for Group Photo Personalization [23] TL;DR: 通过更稳健的 ID association 解决群体照片个性化中的人物身份绑定和错配问题。

Human Parsing, Pose, and Multi-Body Structure Priors

  • Paper Title: Detection, Pose Estimation and Segmentation for Multiple Bodies: Closing the Virtuous Circle [24] TL;DR: 将多人检测、姿态估计和分割互相条件化，可作为 group portrait editing 的结构先验和评估工具。
  • Paper Title: BBoxMaskPose v2: Expanding Mutual Conditioning to 3D [25] TL;DR: 将 bbox、mask、pose 的互条件建模扩展到 3D，有助于讨论复杂群体场景中的空间结构控制。

Topic 2: Layered Image Editing

这一节可以强调 layered representation 为什么能提高可编辑性：对象分离、遮挡关系、区域绑定、layer decomposition 和 layer-level RL。

Layer Decomposition and Inherent Editability

  • Paper Title: Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition [26] TL;DR: 通过图像 layer decomposition 让生成结果天然具备可编辑性，是 layered image generation/editing 的核心代表工作。
  • Paper Title: Stable-Layers: Fine-Tuning Image Layer Decomposition Models with VLM-Scored Reinforcement Learning [27] TL;DR: 用 VLM-scored RL 微调 layer decomposition 模型，说明分层表示也可以通过 reward 优化提升可用性。
  • Paper Title: LayerBind: Layer-wise Instance Binding for Regional and Occlusion Control in Text-to-Image Diffusion Transformers [28] TL;DR: 在 diffusion transformer 中进行 layer-wise instance binding，重点解决区域控制和遮挡控制。

Layered Human Assets and Synthetic Supervision

  • Paper Title: SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling [29] TL;DR: 提供 layered human synthetic data，可作为人物编辑、人体建模和遮挡分解的监督来源。
  • Paper Title: BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion [30] TL;DR: 大规模合成人体数据集，适合支撑全身人物编辑、pose-aware generation 和人体 relighting 的数据讨论。
  • Paper Title: BEDLAM2.0: Synthetic Humans and Cameras in Motion [31] TL;DR: 扩展 BEDLAM 到更复杂的人体和相机运动，为视频人物生成、编辑和 relighting 提供 synthetic supervision。

Topic 3: Relighting

这一节建议先讲数据来源和 lighting representation，再从 general protocols 与 backbone models 两个角度分类方法，最后讨论 evaluation 和 real-world gap。

Dataset Collections and Data Crafting Methods

Light Stage and OLAT Capture

  • Paper Title: Acquiring the Reflectance Field of a Human Face [32] TL;DR: 经典 light stage/reflectance field 工作，奠定了用 OLAT 采集和线性组合做人脸 relighting 的物理基础。
  • Paper Title: POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modeling [33] TL;DR: 构建 portrait OLAT 数据集并提出 illumination-aware 生成框架，是现代人像 relighting 数据与模型结合的代表。

Synthetic Rendering and Data Generation

  • Paper Title: SynthLight: Portrait Relighting with Diffusion Model by Learning to Re-render Synthetic Faces [34] TL;DR: 用 synthetic face re-rendering 训练 diffusion portrait relighting，突出合成数据缓解真实成对 relighting 数据稀缺的问题。
  • Paper Title: BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion [30] TL;DR: 可为 full-body relighting 和人体外观建模提供合成人体、姿态和运动数据。
  • Paper Title: BEDLAM2.0: Synthetic Humans and Cameras in Motion [31] TL;DR: 提供更丰富的动态人体和相机设置，适合视频 relighting 或 dynamic human relighting 的数据背景。
  • Paper Title: SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling [29] TL;DR: layered synthetic human models 可用于分离人体、衣物、几何和外观，对 relighting 的分解式训练有参考价值。

In-the-Wild Data and Real-World Adaptation

  • Paper Title: Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport [35] TL;DR: 通过大规模 in-the-wild 训练和一致 light transport 约束实现 illumination harmonization/editing。
  • Paper Title: Learning a Delighting Prior for Facial Appearance Capture in the Wild [36] TL;DR: 学习 in-the-wild 人脸外观的 delighting prior，用于从自然光图像中恢复更可控的 intrinsic appearance。
  • Paper Title: Toward Real-World Adoption of Portrait Relighting via Hybrid Domain Knowledge Fusion [37] TL;DR: 融合真实域与先验知识，重点解决 portrait relighting 从实验设置走向真实应用的 domain gap。
  • Paper Title: WildRelight: A Real-World Benchmark and Physics-Guided Adaptation for Single-Image Relighting [38] TL;DR: 提供真实世界 single-image relighting benchmark，并用 physics-guided adaptation 缩小真实数据上的性能差距。

Lighting Representation and Control Signal

  • Paper Title: UniLight: A Unified Representation for Lighting [39] TL;DR: 提出统一 lighting representation，可作为 HDR envmap、point light、directional light 等条件编码的综述入口。
  • Paper Title: LightMover: Generative Light Movement with Color and Intensity Controls [40] TL;DR: 关注光源位置、颜色和强度的生成式控制，适合讨论可编辑 lighting parameterization。
  • Paper Title: LightLab: Controlling Light Sources in Images with Diffusion Models [41] TL;DR: 用 diffusion model 控制图像中的光源，是 general image relighting/light editing 的重要代表。

Relighting Methods Categorized by General Protocols

Reverse Rendering and Intrinsic Decomposition

  • Paper Title: Learning a Delighting Prior for Facial Appearance Capture in the Wild [36] TL;DR: 通过学习 delighting prior 将光照影响从人脸外观中分离出来，属于 inverse rendering/decomposition 思路。
  • Paper Title: UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting [42] TL;DR: 联合学习视频分解和重合成，让 relighting 同时保持 temporal consistency 和物理可解释性。
  • Paper Title: GeoRelight: Learning Joint Geometrical Relighting and Reconstruction with Flexible Multi-Modal Diffusion Transformers [43] TL;DR: 将几何重建和 relighting 联合建模，强调 normal/depth/geometry 等多模态条件在 relighting 中的作用。
  • Paper Title: 3DPR: Single Image 3D Portrait Relighting with Generative Priors [44] TL;DR: 利用生成先验从单张图像恢复可 relight 的 3D portrait 表示，连接 single-image relighting 与 3D reconstruction。

OLAT Mixing and Reflectance Field Modeling

  • Paper Title: Acquiring the Reflectance Field of a Human Face [32] TL;DR: 用一组单光源响应线性组合任意光照，是 OLAT mixing 的经典物理协议。
  • Paper Title: POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modeling [33] TL;DR: 将 OLAT 数据与生成模型结合，支持 illumination-aware face modeling 和可控人像 relighting。

Physics-Inspired and Light-Transport-Constrained Methods

  • Paper Title: PI-Light: Physics-Inspired Diffusion for Full-Image Relighting [45] TL;DR: 将物理启发约束注入 diffusion full-image relighting，强调阴影、明暗和全局一致性。
  • Paper Title: Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport [35] TL;DR: 用 consistent light transport 约束提升 diffusion illumination editing 的真实感和一致性。
  • Paper Title: UniLumos: Fast and Unified Image and Video Relighting with Physics-Plausible Feedback [46] TL;DR: 通过 physics-plausible feedback 统一快速图像和视频 relighting。
  • Paper Title: WildRelight: A Real-World Benchmark and Physics-Guided Adaptation for Single-Image Relighting [38] TL;DR: 用 physics-guided adaptation 处理真实世界 relighting 数据与模型泛化问题。

Generative Light Editing and Harmonization

  • Paper Title: LightLab: Controlling Light Sources in Images with Diffusion Models [41] TL;DR: 将光源编辑作为 diffusion image editing 任务，支持改变图像中的光源效果。
  • Paper Title: LightMover: Generative Light Movement with Color and Intensity Controls [40] TL;DR: 允许用户控制光源移动、颜色和强度，强调交互式 lighting editability。
  • Paper Title: SmartPhotoCrafter: Unified Reasoning, Generation and Optimization for Automatic Photographic Image Editing [19] TL;DR: 可作为摄影图像自动优化中的 lighting-aware editing 系统参考。

Reinforcement Learning and Reward-Guided Relighting

  • Paper Title: BiPR-RL: Portrait Relighting via Bi-directional Consistent Deep Reinforcement Learning [47] TL;DR: 用双向一致性强化学习优化 portrait relighting，直接对应 relighting 中 RL 训练策略。
  • Paper Title: Flow-GRPO: Training Flow Matching Models via Online RL [9] TL;DR: 虽不是 relighting 专门方法，但可作为用 lighting reward post-train flow relighting model 的算法基础。
  • Paper Title: EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling [14] TL;DR: 可为 relighting 设计高保真 reward 提供参考，尤其是目标光照、身份保持和视觉真实感的联合评价。
  • Paper Title: Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation [12] TL;DR: 可用于讨论 relighting reward model 的鲁棒性，避免 reward hacking 和不忠实编辑。

Relighting Methods Categorized by Backbone Models

Image Diffusion Models

  • Paper Title: DiFaReli: Diffusion Face Relighting [48] TL;DR: 早期 diffusion face relighting 代表，用扩散先验进行单图人脸重光照。
  • Paper Title: DiFaReli++: Diffusion Face Relighting with Consistent Cast Shadows [49] TL;DR: 在 DiFaReli 基础上强化 cast shadow 一致性，解决人脸 relighting 中阴影可信度问题。
  • Paper Title: SynthLight: Portrait Relighting with Diffusion Model by Learning to Re-render Synthetic Faces [34] TL;DR: 用合成人脸 re-rendering 监督 diffusion relighting，是 synthetic-to-real portrait relighting 的关键方法。
  • Paper Title: PI-Light: Physics-Inspired Diffusion for Full-Image Relighting [45] TL;DR: image diffusion backbone 上加入物理启发机制，实现全图范围的 relighting。
  • Paper Title: LightLab: Controlling Light Sources in Images with Diffusion Models [41] TL;DR: diffusion-based light source control，适合作为通用光照编辑而非仅人像 relighting 的方法。
  • Paper Title: Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport [35] TL;DR: diffusion illumination harmonization/editing 的 in-the-wild 大规模训练代表。

Video Diffusion Models

  • Paper Title: GenLit: Reformulating Single-Image Relighting as Video Generation [50] TL;DR: 将单图 relighting 重新表述为视频生成问题，利用 video generation prior 产生连续光照变化。
  • Paper Title: UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting [42] TL;DR: 面向视频 relighting 的 joint decomposition/synthesis 方法，重点处理时序一致性。
  • Paper Title: UniLumos: Fast and Unified Image and Video Relighting with Physics-Plausible Feedback [46] TL;DR: 统一图像和视频 relighting，并通过 physics-plausible feedback 提升速度与一致性。
  • Paper Title: LightCtrl: Training-free Controllable Video Relighting [51] TL;DR: 提出 training-free 的可控视频 relighting，适合讨论无需额外训练的 video diffusion 控制策略。

Volumetric Rendering Models: NeRF, EG3D, 3DGS, and Avatar Representations

  • Paper Title: VoRF: Volumetric Relightable Faces [52] TL;DR: 用 volumetric representation 表达人脸可重光照外观，是 NeRF-style relightable face 的早期代表。
  • Paper Title: Relightable Gaussian Codec Avatars [53] TL;DR: 将 Gaussian avatar 表示扩展到可重光照外观，连接 3DGS avatar 与 relighting。
  • Paper Title: RelightAnyone: A Generalized Relightable 3D Gaussian Head Model [54] TL;DR: 构建泛化的 relightable 3D Gaussian head model，面向单人头部 avatar relighting。
  • Paper Title: Relightable Holoported Characters: Capturing and Relighting Dynamic Human Performance from Sparse Views [55] TL;DR: 从稀疏视角捕捉并重光照动态人物表演，适合放在 dynamic volumetric human relighting。
  • Paper Title: 3DPR: Single Image 3D Portrait Relighting with Generative Priors [44] TL;DR: 用生成先验补足单图 3D portrait relighting 的不可观测信息。
  • Paper Title: GeoRelight: Learning Joint Geometrical Relighting and Reconstruction with Flexible Multi-Modal Diffusion Transformers [43] TL;DR: 虽使用 diffusion transformer，但核心贡献包含几何重建与 relighting 的联合建模，可在 3D-aware relighting 中讨论。

Evaluation, Benchmarks, and Failure Analysis

  • Paper Title: Do Image Editing Models Understand Lighting? [56] TL;DR: 评估现有 image editing models 是否真正理解光照，可作为 relighting 任务评价和 failure mode 分析的切入点。
  • Paper Title: WildRelight: A Real-World Benchmark and Physics-Guided Adaptation for Single-Image Relighting [38] TL;DR: 提供真实世界 benchmark，适合在实验设置和 real-world generalization 小节重点引用。
  • Paper Title: SpatialReward: Verifiable Spatial Reward Modeling for Fine-Grained Spatial Consistency in Text-to-Image Generation [11] TL;DR: 虽不是 relighting benchmark，但其 verifiable reward 思路可迁移到光源方向、阴影位置和空间一致性评价。

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