1 citations · 1 across the 5 of their papers we have counts for
6 papers
FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur Datasets
Srinjay Sarkar, Prakhar Kaushik, Soumava Paul +1
Realistic and editable animal fur reconstruction from multi-view images is challenging due to fine-scale detail, self-occlusion and obfuscation, and, unlike human hair, the lack of…
NewtPhys: Do Foundation Models Understand Newtonian Physics?
Sebastian Cavada, Soumava Paul, Tuan-Hung Vu +2
Previous work has evaluated physics reasoning in foundation models using synthetic or semi-synthetic scenes and visual question-answering tasks. However, these benchmarks emphasize…
Can These Views Be One Scene? Evaluating Multiview 3D Consistency when 3D Foundation Models Hallucinate
Soumava Paul, Prakhar Kaushik, Alan Yuille
Multiview 3D evaluation assumes that the images being scored are observations of one static 3D scene. This assumption can fail in NVS and sparse-view reconstruction: inputs or gene…
Name That Part: 3D Part Segmentation and Naming
Soumava Paul, Prakhar Kaushik, Ankit Vaidya +2
We address semantic 3D part segmentation: decomposing objects into parts with meaningful names. While datasets exist with part annotations, their definitions are inconsistent acros…
Gaussian Scenes: Pose-Free Sparse-View Scene Reconstruction using Depth-Enhanced Diffusion Priors
Soumava Paul, Prakhar Kaushik, Alan Yuille
In this work, we introduce a generative approach for pose-free (without camera parameters) reconstruction of 360 scenes from a sparse set of 2D images. Pose-free scene reconstructi…
Sp2360: Sparse-view 360 Scene Reconstruction using Cascaded 2D Diffusion Priors
Soumava Paul, Christopher Wewer, Bernt Schiele +1
We aim to tackle sparse-view reconstruction of a 360 3D scene using priors from latent diffusion models (LDM). The sparse-view setting is ill-posed and underconstrained, especially…