ChronoLock adds bounded perturbations to videos that target temporal denoising trajectories in T2V models, reducing unauthorized motion personalization on UCF Sports and HMDB51.
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Dreambooth: Fine tuning text-to-image diffusion models for subject- driven generation
21 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt. However, these models lack the ability to mimic the appearance of subjects in a given reference set and synthesize novel renditions of them in different contexts. In this work, we present a new approach for "personalization" of text-to-image diffusion models. Given as input just a few images of a subject, we fine-tune a pretrained text-to-image model such that it learns to bind a unique identifier with that specific subject. Once the subject is embedded in the output domain of the model, the unique identifier can be used to synthesize novel photorealistic images of the subject contextualized in different scenes. By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, our technique enables synthesizing the subject in diverse scenes, poses, views and lighting conditions that do not appear in the reference images. We apply our technique to several previously-unassailable tasks, including subject recontextualization, text-guided view synthesis, and artistic rendering, all while preserving the subject's key features. We also provide a new dataset and evaluation protocol for this new task of subject-driven generation. Project page: https://dreambooth.github.io/
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UniSim learns a universal real-world simulator from orchestrated diverse datasets, enabling zero-shot deployment of policies trained purely in simulation.
ControlNet adds spatial conditioning controls to pretrained text-to-image diffusion models via zero convolutions for stable fine-tuning on small or large datasets.
PG-MAP formulates inference-time alignment as joint MAP optimization over conditioning c and latent z_t via forward-consistency coupling for diffusion and flow-matching models, reporting gains in PickScore and HPS metrics.
PostureObjectStitch generates assembly-aware anomaly images by decoupling multi-view features into high-frequency, texture and RGB components, modulating them temporally in a diffusion model, and applying conditional loss plus geometric priors to preserve correct component relationships.
IdGlow is a progressive two-stage diffusion framework that uses task-adaptive timestep scheduling, temporal gating, VLM prompt synthesis, and group-level DPO to balance identity preservation and scene coherence in multi-subject image generation.
FLUX.1 Kontext unifies image generation and editing via flow matching and sequence concatenation, delivering improved multi-turn consistency and speed on the new KontextBench benchmark.
DDPO uses policy gradients on the denoising process to optimize diffusion models for arbitrary rewards like human feedback or compressibility.
DiFaReli++ conditions a DDIM on shading references and inferred shadow maps to relight single-view faces with consistent shadows, trained only on 2D images and claiming SOTA on Multi-PIE.
A three-stage fine-tuning process uses human ratings to train a reward model and then improves text-to-image alignment by maximizing reward-weighted likelihood.
An ensemble of stage-specialized text-to-image diffusion models improves prompt alignment over single shared-parameter models while preserving visual quality and inference speed.
ROGLE introduces automated pseudo region-sentence pairs via RSM and multi-granular learning to boost fine-grained alignment in text-based person search, plus the P-VLG benchmark with over 100k annotated regions.
Woosh is a new publicly released foundation model optimized for high-quality sound effect generation from text or video, showing competitive or better results than open alternatives like Stable Audio Open.
SynMotion combines disentangled semantic embeddings, parameter-efficient motion adapters, and alternate subject-motion training on a new SPV dataset to improve motion customization in text-to-video and image-to-video generation.
SOW uses MLLMs and attention to selectively control unidirectional diffusion for pixel-level fidelity and contextual coherence in text-vision-to-image tasks.
PrefPaint uses D3PO and a Model Tree web interface to incorporate gastroenterologist feedback into Stable Diffusion inpainting, producing anatomically accurate polyp images that outperform prior methods in user studies.
TextBoost is a one-shot personalization technique that selectively fine-tunes the text encoder of diffusion models using causality-preserving adaptation and lightweight adapters to reduce parameters and storage.
Stage-aware low-rank scaling and distribution-calibrated candidate selection improve identity consistency in personalized image generation but reduce output diversity.
MythraGen retrieves similar artworks to fine-tune Stable Diffusion via LoRA, producing images that better match text prompts than prior methods on WikiArt.
A literature survey of NeRF and neural field methods from 2020-2025, organized by architecture and application taxonomies with benchmarks and dataset overviews, covering both pre- and post-Gaussian Splatting periods.
citing papers explorer
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ChronoLock: Protecting Videos from Unauthorized Text-to-Video Personalization
ChronoLock adds bounded perturbations to videos that target temporal denoising trajectories in T2V models, reducing unauthorized motion personalization on UCF Sports and HMDB51.
-
Learning Interactive Real-World Simulators
UniSim learns a universal real-world simulator from orchestrated diverse datasets, enabling zero-shot deployment of policies trained purely in simulation.
-
Adding Conditional Control to Text-to-Image Diffusion Models
ControlNet adds spatial conditioning controls to pretrained text-to-image diffusion models via zero convolutions for stable fine-tuning on small or large datasets.
-
PG-MAP: Joint MAP Optimization for Inference-Time Alignment of Diffusion and Flow-Matching Models
PG-MAP formulates inference-time alignment as joint MAP optimization over conditioning c and latent z_t via forward-consistency coupling for diffusion and flow-matching models, reporting gains in PickScore and HPS metrics.
-
PostureObjectstitch: Anomaly Image Generation Considering Assembly Relationships in Industrial Scenarios
PostureObjectStitch generates assembly-aware anomaly images by decoupling multi-view features into high-frequency, texture and RGB components, modulating them temporally in a diffusion model, and applying conditional loss plus geometric priors to preserve correct component relationships.
-
IdGlow: Dynamic Identity Modulation for Multi-Subject Generation
IdGlow is a progressive two-stage diffusion framework that uses task-adaptive timestep scheduling, temporal gating, VLM prompt synthesis, and group-level DPO to balance identity preservation and scene coherence in multi-subject image generation.
-
FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
FLUX.1 Kontext unifies image generation and editing via flow matching and sequence concatenation, delivering improved multi-turn consistency and speed on the new KontextBench benchmark.
-
Training Diffusion Models with Reinforcement Learning
DDPO uses policy gradients on the denoising process to optimize diffusion models for arbitrary rewards like human feedback or compressibility.
-
DiFaReli++: Diffusion Face Relighting with Consistent Cast Shadows
DiFaReli++ conditions a DDIM on shading references and inferred shadow maps to relight single-view faces with consistent shadows, trained only on 2D images and claiming SOTA on Multi-PIE.
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Aligning Text-to-Image Models using Human Feedback
A three-stage fine-tuning process uses human ratings to train a reward model and then improves text-to-image alignment by maximizing reward-weighted likelihood.
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eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
An ensemble of stage-specialized text-to-image diffusion models improves prompt alignment over single shared-parameter models while preserving visual quality and inference speed.
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ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search
ROGLE introduces automated pseudo region-sentence pairs via RSM and multi-granular learning to boost fine-grained alignment in text-based person search, plus the P-VLG benchmark with over 100k annotated regions.
-
Woosh: A Sound Effects Foundation Model
Woosh is a new publicly released foundation model optimized for high-quality sound effect generation from text or video, showing competitive or better results than open alternatives like Stable Audio Open.
-
SynMotion: Semantic-Visual Adaptation for Motion Customized Video Generation
SynMotion combines disentangled semantic embeddings, parameter-efficient motion adapters, and alternate subject-motion training on a new SPV dataset to improve motion customization in text-to-video and image-to-video generation.
-
SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generation
SOW uses MLLMs and attention to selectively control unidirectional diffusion for pixel-level fidelity and contextual coherence in text-vision-to-image tasks.
-
PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback
PrefPaint uses D3PO and a Model Tree web interface to incorporate gastroenterologist feedback into Stable Diffusion inpainting, producing anatomically accurate polyp images that outperform prior methods in user studies.
-
TextBoost: Boosting Text Encoder for Personalized Text-to-Image Generation
TextBoost is a one-shot personalization technique that selectively fine-tunes the text encoder of diffusion models using causality-preserving adaptation and lightweight adapters to reduce parameters and storage.
-
Stage-Aware Adaptation and Distribution Calibration for Subject-Driven Personalized Text-to-Image Generation
Stage-aware low-rank scaling and distribution-calibrated candidate selection improve identity consistency in personalized image generation but reduce output diversity.
-
MythraGen: Two-Stage Retrieval Augmented Art Generation Framework
MythraGen retrieves similar artworks to fine-tune Stable Diffusion via LoRA, producing images that better match text prompts than prior methods on WikiArt.
-
NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)
A literature survey of NeRF and neural field methods from 2020-2025, organized by architecture and application taxonomies with benchmarks and dataset overviews, covering both pre- and post-Gaussian Splatting periods.
- Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systems