[Paper] Alterbute: Editing Intrinsic Attributes of Objects in Images

Source: arXiv - 2601.10714v1

Overview

The paper “Alterbute: Editing Intrinsic Attributes of Objects in Images” presents a diffusion‑based framework that lets you change an object’s core properties—such as color, texture, material, or even shape—while keeping its identity and the surrounding scene intact. By combining a relaxed training objective with fine‑grained visual identity categories (Visual Named Entities), the authors achieve more reliable, controllable edits than prior image‑editing models.

Key Contributions

• Relaxed identity‑preserving training that jointly learns intrinsic (e.g., material) and extrinsic (e.g., pose, background) changes, then clamps extrinsic factors at inference time.
• Visual Named Entities (VNEs): automatically extracted, fine‑grained identity labels (e.g., “Porsche 911 Carrera”) that let the model understand what constitutes an object’s identity.
• Scalable supervision pipeline using a vision‑language model to harvest VNEs and attribute descriptions from large public image collections, eliminating the need for costly manual labeling.
• Demonstrated superiority over existing methods in preserving identity while editing intrinsic attributes, across diverse object categories (vehicles, furniture, apparel, etc.).

Methodology

1. Data Preparation

   • A vision‑language model (e.g., CLIP) scans a massive image dataset, extracts VNE tags (specific model names, product lines) and associated intrinsic attribute captions (“red leather upholstery”, “matte metal finish”).
   • Each training sample includes:
     • an identity reference image (the object we want to keep recognizable),
     • a text prompt describing the desired intrinsic change,
     • a background image and object mask that define extrinsic context.

2. Training Objective

   • The diffusion model is trained to reconstruct the target image conditioned on all three inputs.
   • Crucially, the loss does not penalize extrinsic changes (pose, lighting, background), allowing the network to learn how intrinsic and extrinsic factors interact.

3. Inference Procedure

   • At test time, the original background image and object mask are re‑used, effectively “locking” extrinsic aspects.
   • The model receives the identity reference, the new textual attribute prompt, and the unchanged extrinsic context, producing an edited object that retains its original identity and scene placement.

4. Diffusion Backbone

   • The authors build on a latent diffusion architecture (similar to Stable Diffusion) but augment it with cross‑attention layers that fuse the VNE‑derived identity embedding and the attribute text embedding.

Results & Findings

• Qualitative examples show convincing changes: a silver sedan turned into a matte‑black concept car, a wooden chair rendered with a glossy metal finish, a plain T‑shirt recolored and textured without losing its cut or brand logo.
• Ablation studies confirm that (i) using VNEs dramatically improves identity retention, and (ii) fixing the background/mask at inference is essential for preventing unwanted extrinsic drift.

Practical Implications

• E‑commerce & Virtual Try‑On – Retailers can instantly generate product variants (different colors, materials) from a single photo, reducing the need for costly photoshoots.
• Game Asset Pipelines – Artists can script bulk attribute changes (e.g., “all swords become fire‑enchanted”) while keeping the base model recognizable, accelerating content creation.
• Design Iteration – Industrial designers can explore material or finish swaps on existing renders without rebuilding the 3D model, speeding up the feedback loop.
• Augmented Reality – Real‑time apps could let users “re‑skin” objects in their environment (e.g., change a couch fabric) while preserving spatial coherence.

Limitations & Future Work

• Dependence on Accurate Masks – The method assumes a reasonably clean object mask; poor segmentation can leak extrinsic changes into the edited region.
• VNE Coverage – While the automatic extraction works well for popular consumer goods, niche or custom objects may lack sufficient VNE examples, limiting identity supervision.
• Computation Cost – Diffusion inference remains slower than GAN‑based editors, which may hinder real‑time applications.
• Future directions include integrating more robust segmentation (e.g., interactive matting), expanding VNE vocabularies via web‑scale crawls, and distilling the diffusion model for faster on‑device inference.

Authors

• Tal Reiss
• Daniel Winter
• Matan Cohen
• Alex Rav-Acha
• Yael Pritch
• Ariel Shamir
• Yedid Hoshen

Paper Information

• arXiv ID: 2601.10714v1
• Categories: cs.CV, cs.GR
• Published: January 15, 2026
• PDF: Download PDF