💻 computer science

HomeDiffusion: Zero-Shot Object Customization with Multi-View Representation Learning for Indoor Scenes

HomeDiffusion is a novel zero-shot object customization framework that leverages multi-view representation learning and cross-attention mechanisms within diffusion models to generate visually harmonious, high-fidelity furniture placements in indoor scenes, overcoming the detail loss and pose inconsistencies of existing methods.

Original authors: Guoqiu Li, Jin Song, Yiyun Fei

Published 2026-06-30

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Guoqiu Li, Jin Song, Yiyun Fei

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you have a favorite armchair from your living room, and you want to see what it would look like sitting in a completely different room—maybe a sunny patio or a cozy office. You want it to look exactly like your chair (same fabric, same shape, same little scratches), but you also want it to sit naturally, respecting the angle of the floor and the direction of the light.

This is the problem HomeDiffusion solves. It's a new AI tool that lets you "drop" an object from one photo into another scene without it looking fake, pasted-on, or distorted.

Here is how it works, broken down into simple concepts:

The Problem: The "Pasted" Look

Previous AI tools were like a clumsy painter. If you asked them to put a chair in a new room, they might get the color right, but the chair would look like a flat sticker.

The Perspective Issue: If your chair is asymmetrical (like a sofa with a chaise lounge on the left), and you try to put it in a room where it needs to face the right, old tools would just stretch or twist the image. They didn't understand that the chair has a "3D shape" that needs to rotate, not just stretch.
The Detail Issue: They often lost the fine details. The pattern on the fabric might blur, or the specific curve of the armrest might disappear.

The Solution: HomeDiffusion

The researchers built a system that acts like a master carpenter who also knows 3D geometry. They trained it using a massive library of furniture photos taken from every possible angle (top, side, front, back).

The system works in two main "training camps":

1. The "Mental Rotation" Camp (MORL)

Before the AI can place an object, it needs to understand the object deeply.

The Analogy: Imagine you are trying to describe a complex sculpture to a friend over the phone. If you only show them one photo, they can't guess what the back looks like. But if you show them photos from the front, side, and top, they can build a perfect 3D model in their mind.
What HomeDiffusion does: It takes multiple photos of the same piece of furniture from different angles and teaches the AI to "predict" what the object looks like from any angle, even ones it hasn't seen before. This ensures that when the AI places a sofa in a new room, it knows exactly how the back of the sofa should look, not just the front.

2. The "Seamless Integration" Camp (BOCL)

Once the AI understands the object, it needs to put it into the new scene without it looking like a floating sticker.

The Analogy: Think of this like a high-tech photo collage. But instead of just cutting and pasting, the AI uses a "magic glue" that understands lighting and shadows.
The "HD Visual Encoder": To keep the details sharp (like the weave of a rug or the stitching on a cushion), the AI doesn't just look at the whole picture. It looks at the big picture and zooms in on tiny patches to capture high-definition details.
The "Composite Image" Trick: The AI creates a temporary "fake" image where it pastes the reference object into the new room. It then uses this fake image as a guide.
The "Pixel-Aligned Cross-Attention": This is the secret sauce. Imagine the AI is painting a picture, and it keeps checking a reference photo. Instead of just glancing at the reference, it uses a laser-guided system to match every single pixel of the reference to the new painting. This ensures that the specific pattern on the chair in the new room matches the original chair perfectly, even if the angle is different.

The Results

The paper tested this on furniture (beds, sofas, lamps) and even clothing (virtual try-on).

Better than the competition: Compared to other tools like "Paint-by-Example" or "AnyDoor," HomeDiffusion kept the object's identity much better. It didn't just get the color right; it kept the texture and shape accurate.
No "Training" Required: Unlike some other methods that require you to spend hours "teaching" the AI about your specific chair before you can use it, HomeDiffusion is "zero-shot." You just give it the photos, and it works immediately.
Versatility: Even though it was trained mostly on indoor furniture, it showed it could also work on outdoor scenes and even putting clothes on people (virtual try-on).

In Summary

HomeDiffusion is like having a virtual interior designer who can take a photo of your favorite chair, understand its 3D shape from every angle, and place it into a new room with perfect lighting, shadows, and texture, making it look like it was always there. It solves the "flat sticker" problem by teaching the AI to truly "see" objects in 3D space.

Technical Summary: HomeDiffusion

Problem Statement

Zero-shot object customization aims to seamlessly integrate reference objects into specific areas of a target image, ensuring alignment in layout, lighting, perspective, and spatial relationships while preserving the object's identity and intrinsic details. While existing diffusion-based methods have shown promise, they face two primary challenges in indoor scene customization:

Multi-view Representation: Existing approaches often rely on single-view reference images. For asymmetrical objects, a single view lacks the comprehensive 3D information required to generate poses that harmonize with the background scene's orientation, leading to perspective mismatches.
High-Fidelity Detail Preservation: Methods utilizing standard encoders (e.g., CLIP) or even advanced ones (e.g., DINO-V2) often struggle to retain fine-grained details (patterns, textures, curves) during the generation process, resulting in objects that diverge from the original reference.

Methodology

The authors propose HomeDiffusion, a novel zero-shot framework designed specifically for indoor scenes. The pipeline consists of two distinct training stages:

1. Multi-view Object Representation Learning (MORL)

This stage focuses on learning robust multi-view representations of objects without requiring test-time training.

HD Visual Encoder: To capture fine details without exploding GPU memory usage, the authors introduce an encoder that processes images from both global and local perspectives. A 448×448 image is resized to 224×224 for global feature extraction and split into four 224×224 patches for local high-resolution detail extraction. These features are concatenated and mapped via an MLP to a unified token set.
Self-Generative Training: The model is trained to predict a target viewpoint of a furniture item given a set of $N$ multi-view reference images.
ControlNet Integration: To guide the generation of specific viewpoints, the method employs ControlNet using Canny edge maps of the target view as a conditional layout guide.
Output: A learnable token $C_o$ that encapsulates multi-view object information.

2. Background-driven Object Customization Learning (BOCL)

This stage trains the model to insert the learned object representation into a specific area of a background scene.

Composite Image Guidance: A composite image is created by pasting the reference object into the masked background area. This image contains both scene context and high-fidelity object details.
Latent Space Alignment: The composite image is encoded into the latent space to ensure pixel alignment with the generated image.
Pixel-Aligned Cross-Attention: To transfer high-fidelity details from the composite image to the generation process, the authors introduce a cross-attention mechanism between the feature maps of the composite image and the U-Net encoder at multiple levels. This allows the model to extract and preserve fine details effectively, even when the reference object's viewpoint in the composite image differs from the target viewpoint in the final generation.

Key Contributions

HomeDiffusion Framework: A zero-shot object customization method for indoor scenes that ensures high-fidelity detail preservation and harmonious perspective blending.
HD Visual Encoder: A novel encoder capable of extracting fine object details from both global and local views, balancing resolution and computational efficiency.
Multi-view Object Representation Learning (MORL): A self-generative training process that enables the model to learn robust multi-view representations from multiple viewpoint images, allowing for the generation of objects from unseen angles.
Background-driven Object Customization Learning (BOCL): A method that integrates reference objects into background scenes using composite image guidance and pixel-aligned cross-attention to maintain high-fidelity details.
ZOC-Indoor-Eval Benchmark: The construction of a new benchmark comprising 180k+ synthetic indoor scene images with multi-view furniture data (8 categories) to evaluate zero-shot customization performance.

Experimental Results

The authors evaluated HomeDiffusion on the ZOC-Indoor-Eval benchmark and the Viton-HD benchmark, comparing it against state-of-the-art zero-shot and few-shot methods (Paint-by-Example, AnyDoor, DreamBooth).

Quantitative Performance: On the ZOC-Indoor-Eval benchmark (single-view reference), HomeDiffusion achieved a CLIP Score of 89.4 and a DINO Score of 86.2, outperforming AnyDoor (87.1/83.3) and Paint-by-Example (83.4/78.5).
Multi-view Advantage: When compared to DreamBooth (a few-shot method requiring fine-tuning), HomeDiffusion achieved higher DINO scores across one, three, and five viewpoint scenarios (86.2, 87.5, and 88.1, respectively), demonstrating its ability to leverage multi-view information without test-time training.
Qualitative Analysis: Visual comparisons show that HomeDiffusion successfully generates correct perspectives for asymmetrical objects and preserves fine details (e.g., patterns on headboards, pillow counts) better than competitors, which often suffer from perspective discordance or detail loss.
Ablation Studies: Experiments confirmed the necessity of the MORL stage for handling viewpoint changes and the effectiveness of the HD visual encoder and pixel-aligned cross-attention for detail preservation.
Generalization: Despite being trained on indoor data, the model demonstrated the ability to generalize to outdoor natural scenes and virtual try-on scenarios (Viton-HD).

Significance and Claims

The paper claims that HomeDiffusion addresses the critical limitations of current object customization methods, specifically the inability to handle asymmetrical objects from single views and the loss of high-fidelity details. By leveraging multi-view representation learning and a novel attention mechanism for detail transfer, the method achieves superior performance in zero-shot settings. The authors emphasize that their approach allows users to virtually place e-commerce furniture in personal living spaces with a high degree of fidelity and perspective harmony, bridging the gap between reference objects and target environments without the need for extensive per-object training.