# DreamFit: Garment-Centric Human Generation via a Lightweight Anything-Dressing Encoder

arXiv: https://arxiv.org/abs/2412.17644 · HTML: https://mlsanity.com/paper/2412.17644/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 27 of its 33 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On](https://mlsanity.com/paper/2312.01725/) (2023)
- [Dress Code: High-Resolution Multi-Category Virtual Try-On](https://mlsanity.com/paper/2204.08532/) (2022)
- [LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On](https://mlsanity.com/paper/2305.13501/) (2023)
- [OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on](https://mlsanity.com/paper/2403.01779/) (2024)
- [TryOnDiffusion: A Tale of Two UNets](https://mlsanity.com/paper/2306.08276/) (2023)
- [DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation](https://mlsanity.com/paper/2406.16855/) (2024)
- [LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models](https://mlsanity.com/paper/2403.11627/) (2024)
- [Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images](https://mlsanity.com/paper/2311.16094/) (2023)
- [M&M VTO: Multi-Garment Virtual Try-On and Editing](https://mlsanity.com/paper/2406.04542/) (2024)
- [MV-VTON: Multi-View Virtual Try-On with Diffusion Models](https://mlsanity.com/paper/2404.17364/) (2024)
- [Magic Clothing: Controllable Garment-Driven Image Synthesis](https://mlsanity.com/paper/2404.09512/) (2024)
- [MMTryon: Multi-Modal Multi-Reference Control for High-Quality Fashion Generation](https://mlsanity.com/paper/2405.00448/) (2024)
- [WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on](https://mlsanity.com/paper/2312.03667/) (2023)
- [SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules](https://mlsanity.com/paper/2407.02031/) (2024)
- [Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder](https://mlsanity.com/paper/2407.05552/) (2024)
- [TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On](https://mlsanity.com/paper/2404.00878/) (2024)

## Built on by (most cited first, 9 of 9)

- [IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design](https://mlsanity.com/paper/2504.13176/) (2025)
- [HumanAesExpert: Advancing a Multi-Modality Foundation Model for Human Image Aesthetic Assessment](https://mlsanity.com/paper/2503.23907/) (2025)
- [DreamVVT: Mastering Realistic Video Virtual Try-On in the Wild via a Stage-Wise Diffusion Transformer Framework](https://mlsanity.com/paper/2508.02807/) (2025)
- [OmniDiT: Extending Diffusion Transformer to Omni-VTON Framework](https://mlsanity.com/paper/2603.19643/) (2026)
- [FashionMAC: Deformation-Free Fashion Image Generation with Fine-Grained Model Appearance Customization](https://mlsanity.com/paper/2511.14031/) (2025)
- [PureCC: Pure Learning for Text-to-Image Concept Customization](https://mlsanity.com/paper/2603.07561/) (2026)
- [TAMF-VTON: Texture-Aware Mask-Free Virtual Try-On via High-Fidelity Image Synthesis](https://mlsanity.com/paper/2607.14807/) (2026)
- [WearWow: Native 2K Multi-Garment Virtual Try-On via Adaptive Token Packing and Preference Alignment](https://mlsanity.com/paper/2607.19923/) (2026)
- [BooM-VVT: Boosting Mask-Free Video Virtual Try-On with Image-Level Pseudo Data](https://mlsanity.com/paper/2609.04120/) (2026)

## Abstract (arXiv)

Diffusion models for garment-centric human generation from text or image prompts have garnered emerging attention for their great application potential. However, existing methods often face a dilemma: lightweight approaches, such as adapters, are prone to generate inconsistent textures; while finetune-based methods involve high training costs and struggle to maintain the generalization capabilities of pretrained diffusion models, limiting their performance across diverse scenarios. To address these challenges, we propose DreamFit, which incorporates a lightweight Anything-Dressing Encoder specifically tailored for the garment-centric human generation. DreamFit has three key advantages: (1) Lightweight training: with the proposed adaptive attention and LoRA modules, DreamFit significantly minimizes the model complexity to 83.4M trainable parameters. (2)Anything-Dressing: Our model generalizes surprisingly well to a wide range of (non-)garments, creative styles, and prompt instructions, consistently delivering high-quality results across diverse scenarios. (3) Plug-and-play: DreamFit is engineered for smooth integration with any community control plugins for diffusion models, ensuring easy compatibility and minimizing adoption barriers. To further enhance generation quality, DreamFit leverages pretrained large multi-modal models (LMMs) to enrich the prompt with fine-grained garment descriptions, thereby reducing the prompt gap between training and inference. We conduct comprehensive experiments on both 768 × 512 high-resolution benchmarks and in-the-wild images. DreamFit surpasses all existing methods, highlighting its state-of-the-art capabilities of garment-centric human generation.