53 citations · 312 across the 79 of their papers we have counts for
83 papers
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Subhajit Maity, Ayan Kumar Bhunia, Subhadeep Koley +3
Keypoint detection, integral to modern machine perception, faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailab…
Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch
Aneeshan Sain, Subhajit Maity, Pinaki Nath Chowdhury +3
As sketch research has collectively matured over time, its adaptation for at-mass commercialisation emerges on the immediate horizon. Despite an already mature research endeavour f…
SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models
Subhadeep Koley, Tapas Kumar Dutta, Aneeshan Sain +3
While foundation models have revolutionised computer vision, their effectiveness for sketch understanding remains limited by the unique challenges of abstract, sparse visual inputs…
SketchYourSeg: Mask-Free Subjective Image Segmentation via Freehand Sketches
Subhadeep Koley, Viswanatha Reddy Gajjala, Aneeshan Sain +4
We introduce SketchYourSeg, a novel framework that establishes freehand sketches as a powerful query modality for subjective image segmentation across entire galleries through a si…
Chirpy3D: Part-Aware Multi-View Diffusion for Creative Fine-Grained Object Generation
Kam Woh Ng, Jing Yang, Jia Wei Sii +5
Understanding and generating the fine-grained structure of objects -- such as birds with species-specific beaks, wings, and tails -- is a long-standing challenge in computer vision…
DreamColour: Controllable Video Colour Editing without Training
Chaitat Utintu, Pinaki Nath Chowdhury, Aneeshan Sain +3
Video colour editing is a crucial task for content creation, yet existing solutions either require painstaking frame-by-frame manipulation or produce unrealistic results with tempo…
PartCraft: Crafting Creative Objects by Parts
Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song +1
This paper propels creative control in generative visual AI by allowing users to "select". Departing from traditional text or sketch-based methods, we for the first time allow user…
Do Generalised Classifiers really work on Human Drawn Sketches?
Hmrishav Bandyopadhyay, Pinaki Nath Chowdhury, Aneeshan Sain +4
This paper, for the first time, marries large foundation models with human sketch understanding. We demonstrate what this brings -- a paradigm shift in terms of generalised sketch…
Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval
Aneeshan Sain, Pinaki Nath Chowdhury, Subhadeep Koley +2
In this paper, we delve into the intricate dynamics of Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) by addressing a critical yet overlooked aspect -- the choice of viewpoint…
ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery
Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song +1
Existing fine-grained hashing methods typically lack code interpretability as they compute hash code bits holistically using both global and local features. To address this limitat…
SketchDeco: Training-Free Latent Composition for Precise Sketch Colourisation
Chaitat Utintu, Yi-Zhe Song
We introduce SketchDeco, a training-free approach to sketch colourisation that bridges the gap between professional design needs and intuitive, region-based control. Our method emp…
It's All About Your Sketch: Democratising Sketch Control in Diffusion Models
Subhadeep Koley, Ayan Kumar Bhunia, Deeptanshu Sekhri +4
This paper unravels the potential of sketches for diffusion models, addressing the deceptive promise of direct sketch control in generative AI. We importantly democratise the proce…
You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval
Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain +3
Two primary input modalities prevail in image retrieval: sketch and text. While text is widely used for inter-category retrieval tasks, sketches have been established as the sole p…
Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers
Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain +3
This paper, for the first time, explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR). We highlight a pivotal discovery: the capacity of text…
How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?
Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain +3
In this paper, we propose a novel abstraction-aware sketch-based image retrieval framework capable of handling sketch abstraction at varied levels. Prior works had mainly focused o…
What Sketch Explainability Really Means for Downstream Tasks
Hmrishav Bandyopadhyay, Pinaki Nath Chowdhury, Ayan Kumar Bhunia +3
In this paper, we explore the unique modality of sketch for explainability, emphasising the profound impact of human strokes compared to conventional pixel-oriented studies. Beyond…
SketchINR: A First Look into Sketches as Implicit Neural Representations
Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury +4
We propose SketchINR, to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimen…
IPR-NeRF: Ownership Verification meets Neural Radiance Field
Win Kent Ong, Kam Woh Ng, Chee Seng Chan +2
Neural Radiance Field (NeRF) models have gained significant attention in the computer vision community in the recent past with state-of-the-art visual quality and produced impressi…
DemoCaricature: Democratising Caricature Generation with a Rough Sketch
Dar-Yen Chen, Ayan Kumar Bhunia, Subhadeep Koley +3
In this paper, we democratise caricature generation, empowering individuals to effortlessly craft personalised caricatures with just a photo and a conceptual sketch. Our objective…
Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes
Hmrishav Bandyopadhyay, Subhadeep Koley, Ayan Das +5
In this paper, we democratise 3D content creation, enabling precise generation of 3D shapes from abstract sketches while overcoming limitations tied to drawing skills. We introduce…
DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination
Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song +1
Recent text-to-image (T2I) generative models allow for high-quality synthesis following either text instructions or visual examples. Despite their capabilities, these models face l…
Wired Perspectives: Multi-View Wire Art Embraces Generative AI
Zhiyu Qu, Lan Yang, Honggang Zhang +3
Creating multi-view wire art (MVWA), a static 3D sculpture with diverse interpretations from different viewpoints, is a complex task even for skilled artists. In response, we prese…
SketchDreamer: Interactive Text-Augmented Creative Sketch Ideation
Zhiyu Qu, Tao Xiang, Yi-Zhe Song
Artificial Intelligence Generated Content (AIGC) has shown remarkable progress in generating realistic images. However, in this paper, we take a step "backward" and address AIGC fo…
3D VR Sketch Guided 3D Shape Prototyping and Exploration
Ling Luo, Pinaki Nath Chowdhury, Tao Xiang +2
3D shape modeling is labor-intensive, time-consuming, and requires years of expertise. To facilitate 3D shape modeling, we propose a 3D shape generation network that takes a 3D VR…