# RAVA: Retrieval-Augmented Viewpoint Alignment for Subject-Driven Image Generation

arXiv: https://arxiv.org/abs/2606.17619 · HTML: https://mlsanity.com/paper/2606.17619/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Placed in

- Retrieval-augmented generation / [Multimodal RAG](https://mlsanity.com/rag/multimodal-rag/)

## Builds on (most cited first, 29 of 29)

- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [SyncDreamer: Generating Multiview-consistent Images from a Single-view Image](https://mlsanity.com/paper/2309.03453/) (2023)
- [Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model](https://mlsanity.com/paper/2310.15110/) (2023)
- [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://mlsanity.com/paper/2305.14720/) (2023)
- [SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion](https://mlsanity.com/paper/2403.12008/) (2024)
- [Subject-driven Text-to-Image Generation via Apprenticeship Learning](https://mlsanity.com/paper/2304.00186/) (2023)
- [Re-Imagen: Retrieval-Augmented Text-to-Image Generator](https://mlsanity.com/paper/2209.14491/) (2022)
- [Less-to-More Generalization: Unlocking More Controllability by In-Context Generation](https://mlsanity.com/paper/2504.02160/) (2025)
- [KNN-Diffusion: Image Generation via Large-Scale Retrieval](https://mlsanity.com/paper/2204.02849/) (2022)
- [MV-Adapter: Multi-view Consistent Image Generation Made Easy](https://mlsanity.com/paper/2412.03632/) (2024)
- [Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views](https://mlsanity.com/paper/1505.05641/) (2015)
- [ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusion](https://mlsanity.com/paper/2310.10343/) (2023)
- [Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models](https://mlsanity.com/paper/2207.13038/) (2022)
- [FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept Composition](https://mlsanity.com/paper/2405.13870/) (2024)
- [Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis](https://mlsanity.com/paper/2204.06307/) (2022)
- [Multi-View Image Generation from a Single-View](https://mlsanity.com/paper/1704.04886/) (2017)
- [MC2: Multi-concept Guidance for Customized Multi-concept Generation](https://mlsanity.com/paper/2404.05268/) (2024)
- [RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning](https://mlsanity.com/paper/2502.00848/) (2025)
- [AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation](https://mlsanity.com/paper/2501.09503/) (2025)
- [ViewFusion: Towards Multi-View Consistency via Interpolated Denoising](https://mlsanity.com/paper/2402.18842/) (2024)
- [Diffusion Based Augmentation for Captioning and Retrieval in Cultural Heritage](https://mlsanity.com/paper/2308.07151/) (2023)
- [UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image](https://mlsanity.com/paper/2411.16106/) (2024)
- [Be Decisive: Noise-Induced Layouts for Multi-Subject Generation](https://mlsanity.com/paper/2505.21488/) (2025)
- [FlexGen: Flexible Multi-View Generation from Text and Image Inputs](https://mlsanity.com/paper/2410.10745/) (2024)
- [Improving Subject-Driven Image Synthesis with Subject-Agnostic Guidance](https://mlsanity.com/paper/2405.01356/) (2024)
- [The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented Intervention](https://mlsanity.com/paper/2407.00377/) (2024)
- [Learning Pose Specific Representations by Predicting Different Views](https://mlsanity.com/paper/1804.03390/) (2018)
- [MUSE: Multi-Subject Unified Synthesis via Explicit Layout Semantic Expansion](https://mlsanity.com/paper/2508.14440/) (2025)
- [Cross-modal RAG: Sub-dimensional Text-to-Image Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.21956/) (2025)

## Abstract (arXiv)

Reference-driven image generation has made rapid progress on identity preservation, but reliable viewpoint control across different subjects remains poorly understood. The difficulty is not merely generating a new image of the target subject: the model must infer the implicit viewpoint of one subject and transfer it to another subject using only image-level evidence, without camera poses, depth, or ray-based conditions. In this setting, existing generators conditioned on multiple image references often rely on spurious semantic correlations, which lead to viewpoint drift, part-level structural mismatches, and missing or unsupported target-specific content. We formulate this challenge as cross-subject viewpoint alignment and propose RAVA, a retrieval-augmented framework that supplies explicit geometric evidence before generation. RAVA first learns a cross-instance viewpoint embedding that retrieves target-subject images aligned with the anchor viewpoint, then applies a LogDet-based subset selection strategy to retain a compact reference set that is both view-consistent and structurally complementary. The selected references are finally consumed by a fine-tuned multi-reference image generator. Experiments show that generic semantic embeddings are nearly random for this task, while the proposed retriever substantially improves viewpoint retrieval quality. On cross-subject generation, RAVA consistently outperforms zero-shot baselines and stronger retrieval alternatives under the same generation backbone. These results indicate that cross-subject viewpoint alignment benefits from retrieval-augmented geometric grounding rather than relying on end-to-end generation alone.