Generalization over Memorization: Generalization-Aware Diffusion Adaptation for Single-Image Multi-View Synthesis
GoM framework enhances generalization in single-image multi-view synthesis using scene-disjoint validation and targeted diffusion adaptation, ranking first.
Key Findings
Methodology
The GoM framework combines scene-disjoint validation, exposure-matched selection, and targeted diffusion adaptation. It adapts a 4B rectified-flow DiT using rank-32 LoRA, optimizer restarts, late-checkpoint averaging, and VAE decoder tuning.
Key Results
- In the ACM Multimedia 2026 challenge, GoM achieved a score of 1.8628 on unseen scenes, outperforming other teams.
- Over 300 offline experiments and 24 online submissions demonstrate the importance of validation design and training trajectory control.
- The B2 version, with extended averaging window and high-frequency decoder refinement, performed best on unseen scenes.
Significance
This research addresses the generalization issue in single-image multi-view synthesis, providing a method to improve model performance under limited data conditions, with significant implications for academia and industry.
Technical Contribution
Introduces a novel generalization-first approach, distinct from existing systems relying on geometric parameters and multi-stage generation, offering new engineering possibilities.
Novelty
First to use scene-disjoint validation and targeted diffusion adaptation in single-image multi-view synthesis, differentiating from traditional multi-view training methods.
Limitations
- Structural and perceptual consistency under large viewpoint changes remain challenging.
- Decoder's high-frequency detail recovery is limited.
Future Work
Future work could explore more complex scenes and viewpoint variations, optimizing decoder detail recovery capabilities.
AI Executive Summary
Single-image multi-view synthesis is a challenging task, with existing methods often relying on multi-view training and geometric parameters, limiting their application under limited data conditions. The GoM framework successfully addresses this issue through scene-disjoint validation and targeted diffusion adaptation. The method uses rank-32 LoRA to fine-tune a 4B rectified-flow DiT, enhancing model generalization through optimizer restarts and VAE decoder adjustments. In the ACM Multimedia 2026 challenge, GoM performed excellently on unseen scenes, securing first place. While structural consistency under large viewpoint changes remains an issue, this research provides new directions for future single-image synthesis technologies.
Deep Analysis
Background
Single-image multi-view synthesis is crucial for 3D asset creation, virtual production, and content editing. Traditional methods rely on multi-view training and geometric parameters, limiting generalization under limited data conditions.
Core Problem
The core problem is achieving reliable view transformation under limited data conditions. Existing methods often suffer from memorization when scenes overlap.
Innovation
The GoM framework enhances generalization through scene-disjoint validation and targeted diffusion adaptation, avoiding memorization. It uses rank-32 LoRA to fine-tune a 4B rectified-flow DiT.
Methodology
- �� Scene-disjoint validation: ensures validation measures transferable view control.
- �� Targeted diffusion adaptation: uses rank-32 LoRA to fine-tune DiT.
- �� Optimizer restarts and VAE decoder adjustments: improve model generalization.
Experiments
Experiments conducted on 40 training scenes, assessing the impact of validation design and training trajectory control on model performance. Optimized through over 300 offline experiments and 24 online submissions.
Results
GoM achieved a score of 1.8628 on unseen scenes, outperforming other teams. Extended averaging window and high-frequency decoder refinement improved structural consistency.
Applications
The method can be applied to 3D asset creation and virtual production, especially suitable for scenarios with limited data.
Limitations & Outlook
Structural and perceptual consistency under large viewpoint changes remain challenging. Decoder's high-frequency detail recovery is limited.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. You have a recipe but can only see one step. The GoM framework is like a smart chef who can guess the whole recipe based on the step you see and make a delicious dish. It ensures the final dish tastes and looks great by understanding the role of each step.
ELI14 Explained like you're 14
Imagine you're playing a game and can only see one level. The GoM framework is like a super player who can guess the whole game based on that level and help you win. It ensures you can smoothly pass through by understanding the role of each level.
Glossary
Diffusion Model
A generative model that creates images by gradually denoising. Used as the foundational model for single-image multi-view synthesis.
Used as the foundational model for single-image multi-view synthesis.
LoRA
A parameter-efficient tuning method using low-rank updates to adapt large pretrained models.
Used to fine-tune the 4B rectified-flow DiT.
VAE (Variational Autoencoder)
A generative model that uses an encoder and decoder to generate data.
Used for image reconstruction and generation.
SSIM (Structural Similarity Index)
A metric for evaluating image quality by measuring structural similarity.
Used to assess model's structural consistency.
LPIPS
A perceptual similarity metric that measures image perceptual quality.
Used to assess model's perceptual consistency.
Open Questions Unanswered questions from this research
- 1 How to improve structural consistency under large viewpoint changes?
- 2 How to optimize the decoder for better high-frequency detail recovery?
Applications
Immediate Applications
Virtual Production
Use the GoM framework to enhance the quality and efficiency of scene synthesis in virtual production.
Long-term Vision
3D Asset Creation
Create high-quality 3D assets through single-image multi-view synthesis, advancing virtual reality technology.
Abstract
We present the winning solution to the ACM Multimedia 2026 Grand Challenge on Single-Image Guided Multi-Angle Image Synthesis. It ranks first among 293 registered teams; 56 teams obtained at least one scored submission on the public Phase-A leaderboard. With only 40 training scenes, the challenge requires 26 target views from one RGB model and one forward pass per view; it prohibits explicit geometry, external rendering, chained generation, candidate selection, and post-processing. We identify a critical model-selection failure: shared training and validation scenes make memorization appear as transferable view control. We therefore introduce GoM. Short for Generalization over Memorization, the framework combines scene-disjoint validation, exposure-matched selection, and targeted diffusion adaptation. Its synthesis model adapts a 4B rectified-flow DiT using rank-32 LoRA, optimizer restarts, late-checkpoint averaging, and VAE decoder tuning. More than 300 offline experiments and 24 online submissions show that validation design and training-trajectory control can matter as much as architecture scale in small-data generative modeling.