arXiv subjects

Rameshwar Mishra

Publications and source records attributed to Rameshwar Mishra.

3 recordsLinked to original sources

arXiv2026

RefDiT: Local Attribute Guidance in Reference-Based Image Generation

Personalization models generate new images guided by a few subject references, while style transfer methods aim to produce images aligned with a global style derived from a reference image. Recent approaches perform well when the reference image contains a single object, effectively capturing a global style that encompasses all implicit attributes. However, when applied to complex real-world scenes containing multiple objects with distinct attribute characteristics, these methods, due to their global-level guidance, fail to localize relevant elements in the reference image. The global guidance restricts their ability to generate new images based on the local attributes in the reference image. Moreover, existing methods typically employ a single identifier token to capture all details from the reference, resulting in a lack of individual, attribute-level control. Motivated by these limitations, we propose RefDiT, a novel framework for reference-guided image generation. RefDiT takes as input a reference image, a text prompt, and an optional user-provided guidance context. RefDiT employs local region guidance using the attributes of local elements. It constructs an attribute-aware conditioning signal from the reference image by performing attribute-level decomposition of the identifier token and performs context adjustment in the inference prompt to train low-rank adapter (LoRA) blocks of a diffusion transformer (DiT)-based generative model. RefDiT learns the correspondence between identifier tokens and local regions in the reference image, enabling more effective local guidance.

cs.CV↗

arXiv2026

AD-Relight: Training-Free Banner Relighting via Illumination Translation with Diffusion Priors

The recent surge in content consumption through streaming services has driven a growing demand for personalized content. Personalized advertisements (ads) play a crucial role in enhancing both user engagement and ad effectiveness. A key aspect of ad personalization involves replacing existing regions in a frame with custom, Photoshop-generated banners. However, existing ad-placement pipelines typically rely on simple geometric warping, ignoring the scene's underlying lighting conditions. Similarly, state-of-the-art diffusion-based object insertion and relighting models struggle to accurately relight these newly inserted banners, as they are not trained on ad-banner data, and training such a model for ad banners would require millions of images. This highlights the need for an effective relighting framework that enables seamless integration of custom banners into the original scene. Motivated by this, we present AD-Relight, a novel multi-stage training-free framework that adapts a diffusion-based relighting model at test time to relight newly added Photoshop-generated ad banners. Through extensive evaluation, we demonstrate that AD-Relight outperforms both relighting baselines and existing ad-placement methods based on simple warping. User studies further show that participants consistently prefer the outputs of AD-Relight over those of prior approaches.

cs.CV↗

arXiv2024

Image Synthesis with Graph Conditioning: CLIP-Guided Diffusion Models for Scene Graphs

Advancements in generative models have sparked significant interest in generating images while adhering to specific structural guidelines. Scene graph to image generation is one such task of generating images which are consistent with the given scene graph. However, the complexity of visual scenes poses a challenge in accurately aligning objects based on specified relations within the scene graph. Existing methods approach this task by first predicting a scene layout and generating images from these layouts using adversarial training. In this work, we introduce a novel approach to generate images from scene graphs which eliminates the need of predicting intermediate layouts. We leverage pre-trained text-to-image diffusion models and CLIP guidance to translate graph knowledge into images. Towards this, we first pre-train our graph encoder to align graph features with CLIP features of corresponding images using a GAN based training. Further, we fuse the graph features with CLIP embedding of object labels present in the given scene graph to create a graph consistent CLIP guided conditioning signal. In the conditioning input, object embeddings provide coarse structure of the image and graph features provide structural alignment based on relationships among objects. Finally, we fine tune a pre-trained diffusion model with the graph consistent conditioning signal with reconstruction and CLIP alignment loss. Elaborate experiments reveal that our method outperforms existing methods on standard benchmarks of COCO-stuff and Visual Genome dataset.

cs.CV↗