2 citations · 2 across the 5 of their papers we have counts for
5 papers
ZeST: Zero-Shot Material Transfer from a Single Image
Ta-Ying Cheng, Prafull Sharma, Andrew Markham +2
We propose ZeST, a method for zero-shot material transfer to an object in the input image given a material exemplar image. ZeST leverages existing diffusion adapters to extract imp…
Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition
Chun-Hsiao Yeh, Ta-Ying Cheng, He-Yen Hsieh +6
Recent text-to-image diffusion models are able to learn and synthesize images containing novel, personalized concepts (e.g., their own pets or specific items) with just a few examp…
Learning Continuous 3D Words for Text-to-Image Generation
Ta-Ying Cheng, Matheus Gadelha, Thibault Groueix +4
Current controls over diffusion models (e.g., through text or ControlNet) for image generation fall short in recognizing abstract, continuous attributes like illumination direction…
3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets
Ta-Ying Cheng, Matheus Gadelha, Soren Pirk +4
We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets. Unlike other unsupervised 3D reconstruction methods, we assume that,…
Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction
Ta-Ying Cheng, Hsuan-Ru Yang, Niki Trigoni +2
We present a pose adaptive few-shot learning procedure and a two-stage data interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for single-image 3D reconstructi…