53 citations · 312 across the 79 of their papers we have counts for
20 papers · 1 filter
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…
HeadSculpt: Crafting 3D Head Avatars with Text
Xiao Han, Yukang Cao, Kai Han +5
Recently, text-guided 3D generative methods have made remarkable advancements in producing high-quality textures and geometry, capitalizing on the proliferation of large vision-lan…
SketchXAI: A First Look at Explainability for Human Sketches
Zhiyu Qu, Yulia Gryaditskaya, Ke Li +3
This paper, for the very first time, introduces human sketches to the landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as a ``human-centred'' data form,…
ChiroDiff: Modelling chirographic data with Diffusion Models
Ayan Das, Yongxin Yang, Timothy Hospedales +2
Generative modelling over continuous-time geometric constructs, a.k.a such as handwriting, sketches, drawings etc., have been accomplished through autoregressive distributions. Suc…
Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings
Ayan Kumar Bhunia, Subhadeep Koley, Amandeep Kumar +4
Human sketch has already proved its worth in various visual understanding tasks (e.g., retrieval, segmentation, image-captioning, etc). In this paper, we reveal a new trait of sket…
Picture that Sketch: Photorealistic Image Generation from Abstract Sketches
Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain +3
Given an abstract, deformed, ordinary sketch from untrained amateurs like you and me, this paper turns it into a photorealistic image - just like those shown in Fig. 1(a), all non-…
CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not
Aneeshan Sain, Ayan Kumar Bhunia, Pinaki Nath Chowdhury +3
In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled generalis…
Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style
Fengyin Lin, Mingkang Li, Da Li +3
This paper studies the problem of zero-short sketch-based image retrieval (ZS-SBIR), however with two significant differentiators to prior art (i) we tackle all variants (inter-cat…
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR
Aneeshan Sain, Ayan Kumar Bhunia, Subhadeep Koley +4
This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ~11%. This is…
Data-Free Sketch-Based Image Retrieval
Abhra Chaudhuri, Ayan Kumar Bhunia, Yi-Zhe Song +1
Rising concerns about privacy and anonymity preservation of deep learning models have facilitated research in data-free learning (DFL). For the first time, we identify that for dat…
Generative Model Based Noise Robust Training for Unsupervised Domain Adaptation
Zhongying Deng, Da Li, Junjun He +2
Target domain pseudo-labelling has shown effectiveness in unsupervised domain adaptation (UDA). However, pseudo-labels of unlabeled target domain data are inevitably noisy due to t…
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks
Xiao Han, Xiatian Zhu, Licheng Yu +3
In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and imag…
What Can Human Sketches Do for Object Detection?
Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain +3
Sketches are highly expressive, inherently capturing subjective and fine-grained visual cues. The exploration of such innate properties of human sketches has, however, been limited…
DiffTAD: Temporal Action Detection with Proposal Denoising Diffusion
Sauradip Nag, Xiatian Zhu, Jiankang Deng +2
We propose a new formulation of temporal action detection (TAD) with denoising diffusion, DiffTAD in short. Taking as input random temporal proposals, it can yield action proposals…
Unsupervised Hashing with Similarity Distribution Calibration
Kam Woh Ng, Xiatian Zhu, Jiun Tian Hoe +4
Unsupervised hashing methods typically aim to preserve the similarity between data points in a feature space by mapping them to binary hash codes. However, these methods often over…