Zobrazeno 1 - 10
of 137
pro vyhledávání: '"Sen, Pradeep"'
Creating an appearance-preserving level-of-detail (LoD) representation for arbitrary 3D scenes is a challenging problem. The appearance of a scene is an intricate combination of both geometry and material models, and is further complicated by correla
Externí odkaz:
http://arxiv.org/abs/2409.03761
Virtual try-on (VTO) applications aim to improve the online shopping experience by allowing users to preview garments, before making purchase decisions. However, many VTO tools fail to consider the crucial relationship between a garment's size and th
Externí odkaz:
http://arxiv.org/abs/2408.02803
Autor:
Chen, Sherry X., Vaxman, Yaron, Baruch, Elad Ben, Asulin, David, Moreshet, Aviad, Sra, Misha, Sen, Pradeep
We propose Image Content Appeal Assessment (ICAA), a novel metric that quantifies the level of positive interest an image's content generates for viewers, such as the appeal of food in a photograph. This is fundamentally different from traditional Im
Externí odkaz:
http://arxiv.org/abs/2407.05546
Autor:
Chen, Sherry X., Vaxman, Yaron, Baruch, Elad Ben, Asulin, David, Moreshet, Aviad, Lien, Kuo-Chin, Sra, Misha, Sen, Pradeep
Despite many attempts to leverage pre-trained text-to-image models (T2I) like Stable Diffusion (SD) for controllable image editing, producing good predictable results remains a challenge. Previous approaches have focused on either fine-tuning pre-tra
Externí odkaz:
http://arxiv.org/abs/2404.11120
Physically based rendering of complex scenes can be prohibitively costly with a potentially unbounded and uneven distribution of complexity across the rendered image. The goal of an ideal level of detail (LoD) method is to make rendering costs indepe
Externí odkaz:
http://arxiv.org/abs/2211.05932
Autor:
Xu, Chengyuan, Dong, Boning, Stier, Noah, McCully, Curtis, Howell, D. Andrew, Sen, Pradeep, Höllerer, Tobias
We introduce an interactive image segmentation and visualization framework for identifying, inspecting, and editing tiny objects (just a few pixels wide) in large multi-megapixel high-dynamic-range (HDR) images. Detecting cosmic rays (CRs) in astrono
Externí odkaz:
http://arxiv.org/abs/2204.10356
Recent volumetric 3D reconstruction methods can produce very accurate results, with plausible geometry even for unobserved surfaces. However, they face an undesirable trade-off when it comes to multi-view fusion. They can fuse all available view info
Externí odkaz:
http://arxiv.org/abs/2112.00236
We present 3DVNet, a novel multi-view stereo (MVS) depth-prediction method that combines the advantages of previous depth-based and volumetric MVS approaches. Our key idea is the use of a 3D scene-modeling network that iteratively updates a set of co
Externí odkaz:
http://arxiv.org/abs/2112.00202
Autor:
Ding, Yi, Rich, Alex, Wang, Mason, Stier, Noah, Turk, Matthew, Sen, Pradeep, Höllerer, Tobias
Multimodal classification is a core task in human-centric machine learning. We observe that information is highly complementary across modalities, thus unimodal information can be drastically sparsified prior to multimodal fusion without loss of accu
Externí odkaz:
http://arxiv.org/abs/2111.11992
Rejecting cosmic rays (CRs) is essential for the scientific interpretation of CCD-captured data, but detecting CRs in single-exposure images has remained challenging. Conventional CR detectors require experimental parameter tuning for different instr
Externí odkaz:
http://arxiv.org/abs/2106.14922